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-title: 'Is Data Science a Good Career? Advice From a Pro' -description: 'Is data science a good career choice? Learn from a professional about the benefits, growth potential, and how to thrive in this exciting field.' -authorId: fernando -excludedBySlug: '/ai-data-scientist/career-path' -seo: - title: 'Is Data Science a Good Career? Advice From a Pro' - description: 'Is data science a good career choice? Learn from a professional about the benefits, growth potential, and how to thrive in this exciting field.' - ogImageUrl: 'https://assets.roadmap.sh/guest/is-data-science-a-good-career-10j3j.jpg' -isNew: false -type: 'textual' -date: 2025-01-28 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Is data science a good career choice?](https://assets.roadmap.sh/guest/is-data-science-a-good-career-10j3j.jpg) - -Data science is one of the most talked-about career paths today, but is it the right fit for you? - -With [data science](https://roadmap.sh/ai-data-scientist) at the intersection of technology, creativity, and impact, it can be a very appealing role. It definitely promises high and competitive salaries, and the chance to solve real-world problems. Who would say “no” to that?\! - -But is it the right fit for your skills and aspirations? - -In this guide, I’ll help you uncover the answer to that question by understanding the pros and cons of working as a data scientist. I’ll also look at what the data scientists’ salaries are like and the type of skills you’d need to have to succeed at the job. - -Now sit down, relax, and read carefully, because I’m about to help you answer the question of “Is data science a good career for me?”. - -## Pros of a Career in Data Science - -![Pros of a Career in Data Science](https://assets.roadmap.sh/guest/data-science-pros-4ub43.png) - -There are plenty of “pros” when it comes to picking data science as your career, but let’s take a closer look at the main ones. - -### High Demand and Job Security - -The demand for data scientists has grown exponentially over the past few years and shows no signs of slowing down. According to the [U.S. Bureau of Labor Statistics](https://www.bls.gov/ooh/math/data-scientists.htm), the data science job market is projected to grow by 36% from 2023 to 2033, far outpacing the average for other fields. - -This surge is partly due to the “explosion” of artificial intelligence, particularly tools like ChatGPT, in recent years, which have amplified the need for skilled data scientists to handle complex machine learning models and big data analysis. - -### Competitive Salaries - -One of the most appealing aspects of data science positions is the average data scientist’s salary. Reports from Glassdoor and Indeed highlight that data scientists are among the highest-paid professionals in the technology sector. For example, the national average salary for a data scientist in the United States is approximately $120,000 annually, with experienced professionals earning significantly more. - -These salaries are a reflection of the reality: the high demand for [data science skills](https://roadmap.sh/ai-data-scientist/skills) and the technical expertise required for these roles are not easy to come by. What’s even more, companies in high-cost regions, such as Silicon Valley, New York City, and Seattle, tend to offer premium salaries to attract top talent. - -The financial rewards in this field are usually complemented by additional benefits such as opportunities for professional development like research, publishing, patent registration, etc. - -### Intellectual Challenge and Learning Opportunities - -Data scientists work in a field that demands continuous learning and adaptation to emerging technologies. Their field is rooted in solving complex problems through a combination of technical knowledge, creativity, and critical thinking. In other words, they rarely have any time to get bored. - -What makes data science important and intellectually rewarding, is its ability to address real-world problems. Whether it's optimizing healthcare systems, enhancing customer experiences in retail, or predicting financial risks, data science applications have a tangible impact on people. - -This makes data science a good career for individuals who are passionate about lifelong learning and intellectual stimulation. - -### Versatility - -Data science is a good career choice for those who enjoy variety and flexibility. One of the unique aspects of a career in data science is its ability to reach across various industries and domains (I’m talking technology, healthcare, finance, e-commerce, and even entertainment to name a few). This means data scientists can apply their data science skills in almost any sector that generates or relies on data—which is virtually all industries today. - -## Cons of a Career in Data Science - -![Cons of a Career in Data Science](https://assets.roadmap.sh/guest/data-science-right-fit-x50z5.png) - -The data science job is not without its “cons”, after all, there is no “perfect” role out there. Let’s now review some of the potential challenges that come with the role. - -### Steep Learning Curve - -The steep learning curve in data science is one of the field’s defining characteristics. New data scientists have to develop a deep understanding of technical skills, including proficiency in programming languages like Python, R, and SQL, as well as tools for machine learning and data visualization. - -On top of the already complex subjects to master, data scientists need to find ways of staying current with the constant advancements in the field. This is not optional; it’s a necessity for anyone trying to achieve long-term success in data science. This constant evolution can feel overwhelming, especially for newcomers who are also learning foundational skills. - -Despite these challenges, the steep learning curve can be incredibly rewarding for those who are passionate about solving problems, making data-driven decisions, and contributing to impactful projects. - -While it might sound harsh, it’s important to note that the dedication required to overcome these challenges often results in a fulfilling and (extremely) lucrative career in the world of data science. - -### High Expectations - -Data science positions come with high expectations from organizations. Data scientists usually have the huge responsibility of delivering actionable insights and ensuring these insights are both accurate and timely. - -One of the key challenges data science professionals face is managing the pressure to deliver results under tight deadlines (they’re always tight). Stakeholders often expect instant answers to complex problems, which can lead to unrealistic demands. - -To succeed in such environments, skilled data scientists need strong communication skills to explain their findings and set realistic expectations with stakeholders. - -### Potential Burnout - -The high demand for data science skills usually translates into heavy workloads and tight deadlines, particularly for data scientists working on high-stakes projects (working extra hours is also not an uncommon scenario). - -Data scientists frequently juggle multiple complex responsibilities, such as modeling data, developing machine learning algorithms, and conducting statistical analysis—often within limited timeframes. - -The intense focus required for these tasks, combined with overlapping priorities (and a small dose of poor project management), can lead to mental fatigue and stress. - -Work-life balance can also be a challenge for data scientists giving them another reason for burnout. Combine that with highly active industries, like finance and you have a very hard-to-balance combination. - -To mitigate the risk of burnout, data scientists can try to prioritize setting boundaries, managing workloads effectively (when that’s an option), and advocating for clearer role definitions (better separation of concerns). - -## Skills Required for a Data Science Career - -![Skills Required for a Data Science Career](https://assets.roadmap.sh/guest/data-science-skills-6i284.png) - -To develop a successful career in data science, not all of your [skills](https://roadmap.sh/ai-data-scientist/skills) need to be technical, you also have to look at soft skills, and domain knowledge and to have a mentality of lifelong learning. - -Let’s take a closer look. - -### Technical Skills - -The field of data requires strong foundational technical skills. At the core of these skills is proficiency in programming languages such as Python, R, and SQL. Python is particularly useful and liked for its versatility and extensive libraries, while SQL is essential for querying and managing database systems. R remains a popular choice for statistical analysis and data visualization. - -In terms of frameworks, look into TensorFlow, PyTorch, or Scikit-learn. They’re all crucial for building predictive models and implementing artificial intelligence solutions. Tools like Tableau, Power BI, and Matplotlib are fantastic for creating clear and effective data visualizations, which play a significant role in presenting actionable insights. - -### Soft Skills - -As I said before, it’s not all about technical skills. Data scientists must develop their soft skills, this is key in the field. - -From problem-solving and analytical thinking to developing your communication skills and your ability to collaborate with others. They all work together to help you communicate complex insights and results to other, non-technical stakeholders, which is going to be a key activity in your day-to-day life. - -### Domain Knowledge - -While technical and soft skills are essential, domain knowledge often distinguishes exceptional data scientists from the rest. Understanding industry-specific contexts—such as healthcare regulations, financial market trends, or retail customer behavior—enables data scientists to deliver tailored insights that directly address business needs. If you understand your problem space, you understand the needs of your client and the data you’re dealing with. - -Getting that domain knowledge often involves on-the-job experience, targeted research, or additional certifications. - -### Lifelong Learning - -Finally, if you’re going to be a data scientist, you’ll need to embrace a mindset of continuous learning. The field evolves rapidly, with emerging technologies, tools, and methodologies reshaping best practices. Staying competitive requires consistent professional development through online courses, certifications, conferences, and engagement with the broader data science community. - -Lifelong learning is not just a necessity but also an opportunity to remain excited and engaged in a dynamic and rewarding career. - -## How to determine if data science is right for you? - -![Is data science right for you?](https://assets.roadmap.sh/guest/data-science-right-fit-x50z5.png) - -How can you tell if you’ll actually enjoy working as a data scientist? Even after reading this far, you might still have some doubts. So in this section, I’m going to look at some ways in which you can validate that you’ll enjoy the job of a data scientist before you go through the process of becoming one. - -### Self-Assessment Questions - -Figuring out whether data science is the right career path starts with introspection. Ask yourself the following: - -* Do you enjoy working with numbers and solving complex problems? -* Are you comfortable learning and applying programming skills like Python and SQL? -* Are you excited by the idea of using algorithms to create data-driven insights and actionable recommendations? -* Are you willing to commit to continuous learning in a fast-evolving field? - -Take your time while you think about these questions. You don’t even need a full answer, just try to understand how you feel about the idea of each one. If you don’t feel like saying “yes”, then chances are, this might not be the right path for you (and that’s completely fine\!). - -### Start with Small Projects - -If self-assessment is not your thing, another great way to explore your interest in data science is to dive into small, manageable projects. Platforms like Kaggle offer competitions and publicly available data sets, allowing you to practice exploratory data analysis, data visualization, and predictive modeling. Working on these projects can help you build a portfolio, develop confidence in your skills, and validate that you effectively like working this way. - -Online courses and certifications in data analytics, machine learning, and programming languages provide a structured way to build foundational knowledge. Resources like Coursera, edX, and DataCamp offer beginner-friendly paths to learning data science fundamentals. - -### Network and Seek Mentorship - -Another great way to understand if you would like to be a data scientist, is to ask other data scientists. It might sound basic, but it’s a very powerful way because you’ll get insights about the field from the source. - -Networking, while not easy for everyone, is a key component of entering the data science field. Go to data science meetups, webinars, or conferences to expand your network and stay updated on emerging trends and technologies. - -If you’re not into big groups, try seeking mentorship from data scientists already working in the field. This can accelerate your learning curve. Mentors can offer guidance on career planning, project selection, and skill development. - -## Alternative career paths to consider - -![Alternative career paths to consider](https://assets.roadmap.sh/guest/data-science-alternatives-l7rtz.png) - -Not everyone who is interested in data science wants to pursue the full spectrum of technical skills or the specific responsibilities of a data scientist. Lucky for you, there are several related career paths that can still scratch your itch for fun and interesting challenges while working within the data ecosystem. - -### Data-Adjacent Roles - -* **Data Analyst**: If you enjoy working with data but prefer focusing on interpreting and visualizing it to inform business decisions, a data analyst role might be for you. Data analysts primarily work on identifying trends and providing actionable recommendations without diving deeply into machine learning or predictive modeling. -* **Data Engineer**: If you’re more inclined toward building the infrastructure that makes data science possible, consider becoming a data engineer. These data professionals design, build, and maintain data pipelines, ensuring the accessibility and reliability of large data sets for analysis. The role requires expertise in database systems, data structures, and programming. - -### Related Fields - -* **Software Engineering**: For those who enjoy coding and software development but want to remain close to data-related projects, software engineering offers opportunities to build tools, applications, and systems that support data analysis and visualization. -* **Cybersecurity**: With the increasing emphasis on data privacy and security, cybersecurity professionals play a critical role in protecting sensitive information. This field combines technical knowledge with policy enforcement, making it appealing to those interested in data protection and regulatory compliance. - -### Non-Technical Roles in the Data Ecosystem - -* **Data Governance**: If instead of transforming data and getting insights, you’d like to focus more on how the data is governed (accessed, controlled, cataloged, etc), then this might be the role for you. This role is essential for ensuring that an organization’s data assets are used effectively and responsibly. -* **Data Privacy Office**: In a similar vein to a data governance officer, the data privacy officer cares for the actual privacy of the data. With the rise of AI, data is more relevant than ever, and controlling that you comply with regulations like GDPR and CCPA, is critical for organizations. This role focuses on data privacy strategies, audits, and risk management, making it an excellent fit for those interested in the legal and ethical aspects of data. - -## Next steps - -![Next steps](https://assets.roadmap.sh/guest/data-science-next-steps-o539b.png) - -Data science is a promising career path offering high demand, competitive salaries, and multiple opportunities across various industries. Its ability to address real-world problems, combined with the intellectual challenge it presents, makes it an attractive choice for many. However, it also makes it a very difficult and taxing profession for those who don’t enjoy this type of challenge. - -There are many potential next steps for you to take and answer the question of “Is data science a good career?”. - -For example, you can reflect on your interests and strengths. Ask yourself whether or not you enjoy problem-solving, working with data sets, and learning new technologies. Use this reflection to determine if data science aligns with your career goals. - -You can also consume resources like the [AI/Data Scientist roadmap](https://roadmap.sh/ai-data-scientist) and the [Data Analyst roadmap](https://roadmap.sh/data-analyst), as they offer a clear progression for developing essential skills, so check them out. These tools can help you identify which areas to focus on based on your current expertise and interests. - -In the end, just remember: data science is rapidly evolving so make sure to stay engaged by reading research papers, following industry blogs, or attending conferences. Anything you can do will help, just figure out what works for you and keep doing it. diff --git a/src/data/guides/ai-data-scientist-lifecycle.md b/src/data/guides/ai-data-scientist-lifecycle.md deleted file mode 100644 index 9414ddf32..000000000 --- a/src/data/guides/ai-data-scientist-lifecycle.md +++ /dev/null @@ -1,200 +0,0 @@ ---- -title: "Data Science Lifecycle 101: A Beginners' Ultimate Guide" -description: 'Discover the Data Science Lifecycle step-by-step: Learn key phases, tools, and techniques in this beginner-friendly guide.' -authorId: fernando -excludedBySlug: '/ai-data-scientist/career-path' -seo: - title: "Data Science Lifecycle 101: A Beginners' Ultimate Guide" - description: 'Discover the Data Science Lifecycle step-by-step: Learn key phases, tools, and techniques in this beginner-friendly guide.' - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-lifecycle-eib3s.jpg' -isNew: false -type: 'textual' -date: 2025-01-29 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Data science lifecycle 101](https://assets.roadmap.sh/guest/data-science-lifecycle-eib3s.jpg) - -Developing a data science project, from beginning to production is not a trivial task. It involves so many steps and so many complex tasks, that without some guardrails, releasing to production becomes ten times harder. - -Here’s where the data science lifecycle comes into play. It brings a structured approach so that [data scientists](https://roadmap.sh/ai-data-scientist), data analysts, and others can move forward together from raw data to actionable insights. - -In this guide, we’ll cover everything you need to know about the data science lifecycle, its many variants, and how to pick the right one for your project. - -So let’s get going\! - -## Core Concepts of a Lifecycle - -![Core concepts of a data science lifecycle](https://assets.roadmap.sh/guest/core-concepts-bn8g0.png) - -To fully understand the concept of the lifecycle, we have to look at the core concepts inside this framework, and how they contribute to the delivery of a successful data science project. - -### Problem Definition - -Every data science project begins with a clear definition of the problem to be solved. This involves collaborating with key stakeholders to identify objectives and desired outcomes. Data scientists must understand the context and scope of the project to ensure that the goals align with business or research needs. - -### Data Collection - -In the data collection phase, data scientists and data engineers work together and gather relevant data from diverse data sources. This includes both structured and unstructured data, such as historical records, new data, or data streams. - -The process ensures the integration of all pertinent data, creating a robust dataset for the following stages. Data acquisition tools and strategies play a critical role in this phase. - -### Data Preparation - -This stage addresses the quality of raw data by cleaning and organizing it for analysis. Tasks such as treating inaccurate data, handling missing values, and converting raw data into usable formats are central to this stage. This stage prepares the data for further and more detailed analysis. - -### Exploratory Data Analysis (EDA) - -The exploratory data analysis stage is where the “data processing” happens. This stage focuses on uncovering patterns, trends, and relationships within the data. Through data visualization techniques such as bar graphs and statistical models, data scientists perform a thorough data analysis and gain insights into the data’s structure and characteristics. - -Like every stage so far, this one lays the foundation for the upcoming stages. In this particular case, after performing a detailed EDA, data scientists have a much better understanding of the data they have to work with, and a pretty good idea of what they can do with it now. - -### Model Building and Evaluation - -The model building phase involves developing predictive or machine learning models tailored to the defined problem. Data scientists experiment with various machine learning algorithms and statistical models to determine the best approach. Here’s where data modeling happens, bridging the insights gained during the exploratory data analysis (EDA) phase with actionable predictions and outcomes used in the deployment phase. - -Model evaluation follows, where the performance and accuracy of these models are tested to ensure reliability. - -### Deployment and Monitoring - -The final stage of this generic data science lifecycle involves deploying the model into a production environment. Here, data scientists, machine learning engineers, and quality assurance teams ensure that the model operates effectively within existing software systems. - -After this stage, continuous monitoring and maintenance are essential to address new data or changing conditions, which can impact the performance and accuracy of the model. - -## Exploring 6 Popular Lifecycle Variants - -![6 popular data science lifecycles](https://assets.roadmap.sh/guest/alternative-lifecycles-wdvp1.png) - -The data science lifecycle offers various frameworks tailored to specific needs and contexts. Below, we explore six prominent variants: - -### CRISP-DM (Cross Industry Standard Process for Data Mining) - -CRISP-DM is one of the most widely used frameworks in data science projects, especially within business contexts. - -It organizes the lifecycle into six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. - -This iterative approach allows teams to revisit and refine previous steps as new insights emerge. CRISP-DM is ideal for projects where aligning technical efforts with business goals is very important. - -**Example use case**: A retail company wants to improve customer segmentation for targeted marketing campaigns. Using CRISP-DM, the team starts with business understanding to define segmentation goals, gathers transaction and demographic data, prepares and cleans it, builds clustering models, evaluates their performance, and deploys the best model to group customers for personalized offers. - -### KDD (Knowledge Discovery in Databases) - -The KDD process focuses on extracting useful knowledge from large datasets. Its stages include Selection, Preprocessing, Transformation, Data Mining, and Interpretation/Evaluation. - -KDD emphasizes the academic and research-oriented aspects of data science, making it an ideal choice for experimental or exploratory projects in scientific domains. It offers a systematic approach to discovering patterns and insights in complex datasets. - -**Example use case:** A research institute analyzes satellite data to study climate patterns. They follow KDD by selecting relevant datasets, preprocessing to remove noise, transforming data to highlight seasonal trends, applying data mining techniques to identify long-term climate changes, and interpreting results to publish findings. - -### Data Analytics Lifecycle - -This specific data science lifecycle is tailored for enterprise-level projects that prioritize actionable insights. It’s composed of six stages: Discovery, Data Preparation, Model Planning, Model Building, Communicating Results, and Operationalizing. - -The framework’s strengths lie in its alignment with business objectives and readiness for model deployment, making it ideal for organizations seeking to integrate data-driven solutions into their operations. - -**Example use case:** A financial institution uses the Data Analytics Lifecycle to detect fraudulent transactions. They discover patterns in historical transaction data, prepare it by cleaning and normalizing, plan predictive models, build and test them, communicate results to fraud prevention teams, and operationalize the model to monitor real-time transactions. - -### SEMMA (Sample, Explore, Modify, Model, Assess) - -SEMMA is a straightforward and tool-centric framework developed by SAS. It focuses on sampling data, exploring it for patterns, modifying it for analysis, modeling it for predictions, and assessing the outcomes. - -This lifecycle is particularly useful for workflows involving specific analytics tools. Its simplicity and strong emphasis on data exploration make it an excellent choice for teams prioritizing rapid insights. - -**Example use case:** A healthcare organization predicts patient readmission rates using SEMMA. They sample data from hospital records, explore patient histories for trends, modify features like patient age and diagnoses, build machine learning models, and assess their accuracy to choose the most effective predictor. - -### Team Data Science Process (TDSP) - -TDSP offers a collaborative and agile framework that organizes the lifecycle into four key stages: Business Understanding, Data Acquisition, Modeling, and Deployment. - -Designed with team-based workflows in mind, TDSP emphasizes iterative progress and adaptability, ensuring that projects align with business needs while remaining flexible to changes. It’s well-suited for scenarios requiring close collaboration among data scientists, engineers, and stakeholders. - -**Example use case:** A logistics company improves delivery route optimization. Using TDSP, the team collaborates to understand business goals, acquires data from GPS and traffic systems, develops routing models, and deploys them to dynamically suggest the fastest delivery routes. - -### MLOps Lifecycle - -MLOps focuses specifically on machine learning operations and production environments. Its stages include Data Engineering, Model Development, Model Deployment, and Monitoring. - -This lifecycle is essential for projects involving large-scale machine learning systems that demand high scalability and automation. - -MLOps integrates seamlessly with continuous integration and delivery pipelines, ensuring that deployed models remain effective and relevant as new data is introduced. - -Each of these frameworks has its own strengths and is suited to different types of data science operations. - -**Example use case:** An e-commerce platform deploys a recommendation engine using MLOps. They engineer data pipelines from user activity logs, develop collaborative filtering models, deploy them on the website, and monitor their performance to retrain models when new user data is added. - -## How to Choose the Right Data Science Lifecycle - -![How to pick a data science lifecycle](https://assets.roadmap.sh/guest/how-to-pick-a-lifecycle-q9t3m.png) - -Determining the most suitable data science lifecycle for your data science project requires a systematic approach. After all, not all lifecycles are best suited for all situations. - -You can follow these steps to identify the framework that aligns best with your goals and resources: - -1. **Define your objectives:** Clearly identify the goals of your project. Are you solving a business problem, conducting academic research, or deploying a machine learning model? Understanding the end objective will narrow down your choices. -2. **Assess project complexity:** Evaluate the scope and intricacy of your project. Simple projects may benefit from streamlined frameworks like SEMMA, while complex projects with iterative requirements might need CRISP-DM or TDSP. -3. **Evaluate your team composition:** Consider the expertise within your team. A team with strong machine learning skills may benefit from MLOps, whereas a diverse team with varying levels of experience might prefer a more general framework like CRISP-DM. -4. **Analyze industry and domain requirements:** Different industries may have unique needs. For example, business-driven projects often align with the Data Analytics Lifecycle, while academic projects might find KDD more suitable. -5. **Examine available tools and resources:** Ensure that the tools, software, and infrastructure you have access to are compatible with your chosen lifecycle. Frameworks like SEMMA may require specific tools such as SAS. -6. **Match to key stakeholder needs:** Align the lifecycle with the expectations and requirements of stakeholders. A collaborative framework like TDSP can be ideal for projects needing frequent input and iteration with business partners. -7. **Run a trial phase:** If possible, test a smaller project or a subset of your current project with the selected framework. This will help you assess its effectiveness and make adjustments as needed. - -Follow these steps and you can identify the lifecycle that not only suits your project but also ensures that your data science process is efficient and productive. Each project is unique, so tailoring the lifecycle to its specific demands is critical to success. - -## Generic Framework for Beginners - -![Generic framework for beginners](https://assets.roadmap.sh/guest/basic-lifecycle-nlw8q.png) - -While there are many different data science lifecycles and ways to tackle data science projects, if you’re just getting started and you’re trying to push your first project into production, relying on a beginner-friendly lifecycle might be a better idea. - -A generic framework for beginners in data science simplifies the lifecycle into manageable steps, making it easier to understand and implement. You can follow these steps to define your new framework: - -### 1\. Define the problem - -![Define the problem](https://assets.roadmap.sh/guest/lifecycle-define-problem-6snat.png) - -Start by clearly identifying the problem you aim to solve. Consider the objectives and outcomes you want to achieve, and ensure these are aligned with the needs of any stakeholder. This will help focus your efforts during development and set the right expectations with your stakeholders. - -### 2\. Collect and clean data - -![Collect and clean data](https://assets.roadmap.sh/guest/lifecycle-collect-data-8k25a.png) - -Gather data from reliable and relevant sources. During this stage, focus on ensuring data quality by treating inaccurate data, filling in missing values, validating and removing potential data biases and finally, converting raw data into usable formats. - -### 3\. Analyze and visualize - -![Analyze and visualize](https://assets.roadmap.sh/guest/lifecycle-analize-visualize-v07ml.png) - -Explore the data to uncover patterns, trends, and insights. Use simple data visualization techniques such as bar graphs and scatter plots, along with basic statistical methods, to gain a deeper understanding of the dataset’s structure and variables. - -### 4\. Build and evaluate a model - -![Build and evaluate a model](https://assets.roadmap.sh/guest/lifecycle-build-models-0rn42.png) - -Develop a basic predictive model using accessible machine learning or statistical tools. Test the model’s performance to ensure it meets the objectives defined earlier during step 1\. For beginners, tools with user-friendly interfaces like Python libraries or Excel can be highly effective. - -### 5\. Share results and deploy - -![Share results and deploy](https://assets.roadmap.sh/guest/lifecycle-share-results-016nc.png) - -Present your findings to stakeholders in a clear and actionable format. If applicable, deploy the model into a small-scale production environment to observe its impact and gather feedback for further improvement. - -**Tips for small projects:** Start with a problem you’re familiar with, such as analyzing personal expenses or predicting simple outcomes. Focus on learning the process rather than achieving perfect results. Use open-source tools and resources to experiment and build your confidence. - -Use this framework if this is your first data science project, evaluate your results, and most importantly, reflect on your experience. - -Take those insights into your next project and decide if for that one you would actually benefit from using one of the predefined standard lifecycles mentioned above. - -## Conclusion - -The data science lifecycle is a cornerstone of modern data science. By understanding its stages and principles, professionals can navigate the complexities of data science projects with confidence. - -Regardless of what you’re doing, dealing with unstructured data, creating models, or deploying machine learning algorithms, the lifecycle provides a roadmap for success. - -As data science experts and teams continue to explore and refine their approaches, the lifecycle framework remains a key tool for achieving excellence in any and all operations. - -Finally, remember that if you’re interested in developing your data science career, you have our [data scientist](https://roadmap.sh/ai-data-scientist) and [data analyst](https://roadmap.sh/data-analyst) roadmaps at your disposal. These roadmaps will help you focus your learning time on the really important and relevant topics. \ No newline at end of file diff --git a/src/data/guides/ai-data-scientist-skills.md b/src/data/guides/ai-data-scientist-skills.md deleted file mode 100644 index fd25ce060..000000000 --- a/src/data/guides/ai-data-scientist-skills.md +++ /dev/null @@ -1,197 +0,0 @@ ---- -title: 'Top 11 Data Science Skills to Master in @currentYear@' -description: 'Looking to excel in data science? Learn the must-have skills for @currentYear@ with our expert guide and advance your data science career.' -authorId: fernando -excludedBySlug: '/ai-data-scientist/skills' -seo: - title: 'Top 11 Data Science Skills to Master in @currentYear@' - description: 'Looking to excel in data science? Learn the must-have skills for @currentYear@ with our expert guide and advance your data science career.' - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-skills-to-master-q36qn.jpg' -isNew: false -type: 'textual' -date: 2025-01-28 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Top data science skills](https://assets.roadmap.sh/guest/data-science-skills-to-master-q36qn.jpg) - -Data science is becoming more relevant as a field and profession by the day. Part of this constant change is the mind-blowing speed at which AI is evolving these days. Every day a new model is released, every week a new product is built around it, and every month OpenAI releases an earth-shattering change that pushes the field even further than before. - -Data scientists sit at the core of that progress, but what does it take to master the profession? - -Mastering the essential data scientist skills goes beyond just solving complex problems. It includes the ability to handle data workflows, build machine learning models, and interpret data trends effectively. - -In this guide, we'll explore the top 10 skills that future data scientists must work on to shine brighter than the rest in 2025, setting a foundation for long-term success. - -These are the data scientist skills covered in the article: - -* Programming proficiency with **Python, R, and SQL** -* Data manipulation and analysis, including **data wrangling** and **exploratory data analysis** -* Mastery of **machine learning** and **AI techniques** -* Strong statistical and **mathematical** **foundations** -* Familiarity with **big data technologies** -* Data engineering for infrastructure and **ETL pipelines** -* Expertise in **data visualization** with tools like **Plotly** and **D3.js** -* **Domain knowledge** for aligning data science projects with business goals -* **Soft skills** for communication, collaboration, and creativity -* **Feature engineering** and selection for **model optimization**. -* Staying current with trends like **MLOps** and **Generative AI.** - -## **Understanding Data Science** - -[Data science](https://roadmap.sh/ai-data-scientist) is an interdisciplinary field that combines multiple disciplines to make sense of data and drive actionable insights. It integrates programming, statistical analysis, and domain knowledge to uncover patterns and trends in both structured and unstructured data. This powerful combination enables data professionals to solve a variety of challenges, such as: - -* Building predictive models to forecast sales or identify customer churn. -* Developing optimization techniques to streamline supply chains or allocate resources more effectively. -* Leveraging automation and artificial intelligence to create personalized recommendations or detect fraudulent activity in massive datasets. - -At its core, data science empowers organizations to turn raw data into actionable insights. By interpreting data effectively and applying statistical models, data scientists support data-driven decision-making, ensuring businesses maintain a competitive edge. - -The data science field requires a unique mix of technical skills, analytical prowess, and creativity to handle the vast array of complex data sets encountered in real-world scenarios. In other words, being a data scientist is not for everyone. - -**1\. Programming Proficiency** - -![Programming proficiency with Python, R and SQL](https://assets.roadmap.sh/guest/r-sql-python-qqm69.png) - -Programming remains a cornerstone of the data science field, forming the foundation for nearly every task in data science projects. Mastery of programming languages like Python, R, and SQL is crucial for aspiring data scientists to handle data workflows effectively. - -Python is the undisputed leader in data science, thanks to its extensive libraries and frameworks. Pandas, NumPy, and Scikit-learn are essential for tasks ranging from data wrangling and numerical analysis to building machine learning models. Deep learning tools such as TensorFlow and PyTorch make Python indispensable for tackling advanced challenges like developing artificial neural networks for image recognition and natural language processing (NLP). - -R excels in statistical analysis and visualization. Its specialized libraries, like ggplot2 for data visualization and caret for machine learning models, make it a preferred choice for academics and data analysis tasks that require interpreting data trends and creating statistical models. - -SQL is the backbone of database management, which is essential for extracting, querying, and preparing data from structured databases. A strong command of SQL allows data professionals to manage massive datasets efficiently and ensure smooth integration with analytical tools. - -## **2\. Data Manipulation and Analysis** - -![Data manipulation and wrangling](https://assets.roadmap.sh/guest/data-wrangling-zwd5i.png) - -The ability to manipulate and analyze data lies at the heart of data science skills. These tasks involve transforming raw data into a format suitable for analysis and extracting insights through statistical concepts and exploratory data analysis (EDA). - -Data wrangling is a critical skill for cleaning and preparing raw data, addressing missing values, and reshaping complex data sets. For example, consider a dataset containing customer transaction records with incomplete information. Using tools like Pandas in Python, a data scientist can identify missing values, impute or drop them as appropriate, and restructure the data to focus on specific variables like transaction frequency or total purchase amounts. This process ensures the dataset is ready for meaningful analysis. - -Tools like Pandas, PySpark, and Dask are invaluable for handling unstructured data or working with massive datasets efficiently. These tools allow data scientists to transform complex data sets into manageable and analyzable forms, which is foundational for building machine learning models or conducting advanced statistical analysis. - -Performing exploratory data analysis allows data scientists to identify patterns, correlations, and anomalies within structured data. Visualization libraries like Matplotlib and Seaborn, combined with Python scripts, play a significant role in understanding data insights before building predictive models or statistical models. - -**3\. Machine Learning and AI** - -![Machine learning and AI techniques](https://assets.roadmap.sh/guest/machine-learning-odmf1.png) - -Machine learning is a driving force in the data science industry, enabling data-driven decisions across sectors and revolutionizing how organizations interpret data and make predictions. Mastering machine learning algorithms and frameworks are among the top data science skills for aspiring data scientists who wish to excel in analyzing data and creating impactful solutions. - -Data scientists commonly tackle supervised learning tasks, such as predicting housing prices through regression models or identifying fraudulent transactions with classification algorithms. For example, using Scikit-learn, a data scientist can train a decision tree to categorize customer complaints into predefined categories for better issue resolution. Additionally, unsupervised techniques like clustering are applied in market segmentation to group customers based on purchasing patterns, helping businesses make data-driven decisions. - -Deep learning represents the cutting edge of artificial intelligence, utilizing artificial neural networks to manage unstructured data and solve highly complex problems. Frameworks like TensorFlow and PyTorch are essential tools for developing advanced solutions, such as NLP models for chatbot interactions or generative AI for creating realistic images. These tools empower data scientists to push the boundaries of innovation and unlock actionable insights from vast and complex datasets. - -## **4\. Statistical and Mathematical Foundations** - -![Statistical and mathematical foundations](https://assets.roadmap.sh/guest/statistical-analysis-bnkn3.png) - -Statistical concepts and mathematical skills form the backbone of building robust data models and interpreting data insights. These foundational skills are indispensable for anyone aiming to succeed in the data science field. - -Probability theory and hypothesis testing play a vital role in understanding uncertainty in data workflows. For instance, a data scientist might use hypothesis testing to evaluate whether a new marketing strategy leads to higher sales compared to the current approach, ensuring data-driven decision-making. - -Linear algebra and calculus are crucial for developing and optimizing machine learning algorithms. Techniques like matrix decomposition and gradient descent are used to train neural networks and enhance their predictive accuracy. These mathematical tools are the engine behind many advanced algorithms, making them essential data scientist skills. - -Advanced statistical analysis, including A/B testing and Bayesian inference, helps validate predictions and understand relationships within complex datasets. For example, A/B testing can determine which website design yields better user engagement, providing actionable insights to businesses. - -## **5\. Big Data Technologies** - -![Big data technologies](https://assets.roadmap.sh/guest/big-data-x0rmx.png) - -While big data skills are secondary for most data scientists, understanding big data technologies enhances their ability to handle massive datasets efficiently. Familiarity with tools like Apache Spark and Hadoop allows data scientists to process and analyze distributed data, which is especially important for projects involving millions of records. For example, Apache Spark can be used to calculate real-time metrics on user behavior across e-commerce platforms, enabling businesses to personalize experiences dynamically. - -Cloud computing skills, including proficiency with platforms like AWS or GCP, are also valuable for deploying machine learning projects at scale. A data scientist working with GCP's BigQuery can query massive datasets in seconds, facilitating faster insights for time-sensitive decisions. These technologies, while not the core of a data scientist's responsibilities, are crucial for ensuring scalability and efficiency in data workflows. - -## **6\. Data Engineering** - -![Data engineering](https://assets.roadmap.sh/guest/data-engineering-lqm7i.png) - -Data engineering complements data science by creating the infrastructure required to analyze data effectively. This skill set ensures that data flows seamlessly through pipelines, enabling analysis and decision-making. - -Designing ETL (Extract, Transform, Load) pipelines is a critical part of data engineering. For instance, a data engineer might create a pipeline to collect raw sales data from multiple sources, transform it by standardizing formats and handling missing values, and load it into a database for further analysis. These workflows are the backbone of data preparation. - -Using tools like Apache Airflow, those workflows can be streamlined, while managing real-time data streaming using Kafka ensures that real-time data—such as social media feeds or IoT sensor data—is processed without delay. For example, a Kafka pipeline could ingest weather data to update forecasts in real-time. - -Finally, storing and querying complex data sets in cloud computing with tools like Snowflake or BigQuery allows data scientists to interact with massive datasets effortlessly. - -These platforms support scalable storage and high-performance queries, enabling faster analysis and actionable insights. - -## **7\. Data Visualization** - -![Data visualization techniques and tools](https://assets.roadmap.sh/guest/data-visualization-bkkel.png) - -Data visualization is a cornerstone of the data science field, as it enables data professionals to present data and communicate findings effectively. While traditional tools like Tableau and Power BI are widely used, aspiring data scientists should prioritize programming-based tools like Plotly and D3.js for greater flexibility and customization. - -For example, using Plotly, a data scientist can create an interactive dashboard to visualize customer purchase trends over time, allowing stakeholders to explore the data dynamically. Similarly, D3.js offers unparalleled control for designing custom visualizations, such as heatmaps or network graphs, that convey complex relationships in a visually compelling manner. - -Applying storytelling techniques further enhances the impact of visualizations. By weaving data insights into a narrative, data scientists can ensure their findings resonate with stakeholders and drive actionable decisions. For instance, a well-crafted story supported by visuals can explain how seasonal demand patterns affect inventory management, bridging the gap between technical analysis and strategic planning. - -## **8\. Business and Domain Knowledge** - -![Business and domain knowledge](https://assets.roadmap.sh/guest/business-and-domain-knowledge-5mnne.PNG) - -Domain knowledge enhances the relevance of data science projects by aligning them with organizational goals and addressing unique industry-specific challenges. Understanding the context in which data is applied allows data professionals to make their analysis more impactful and actionable. - -For example, in the finance industry, a data scientist with domain expertise can design predictive models that assess credit risk by analyzing complex data sets of customer transactions, income, and past credit behavior. These models enable financial institutions to make data-driven decisions about lending policies. - -In healthcare, domain knowledge allows data scientists to interpret medical data effectively, such as identifying trends in patient outcomes based on treatment history. By leveraging data models tailored to clinical needs, data professionals can help improve patient care and operational efficiency in hospitals. - -This alignment ensures that insights are not only technically robust but also directly applicable to solving real-world problems, making domain knowledge an indispensable skill for data professionals seeking to maximize their impact. - -## **9\. Soft Skills** - -![Soft skills for data scientists](https://assets.roadmap.sh/guest/soft-skills-3y4zs.png) - -Soft skills are as essential as technical skills in the data science field, bridging the gap between complex data analysis and practical implementation. These skills enhance a data scientist's ability to communicate findings, collaborate with diverse teams, and approach challenges with innovative solutions. - -**Communication** is critical for translating data insights into actionable strategies. For example, a data scientist might present the results of an exploratory data analysis to marketing executives, breaking down statistical models into simple, actionable insights that drive campaign strategies. The ability to clearly interpret data ensures that stakeholders understand and trust the findings. - -**Collaboration** is equally vital, as data science projects often involve cross-functional teams. For instance, a data scientist might work closely with software engineers to integrate machine learning models into a production environment or partner with domain experts to ensure that data-driven decisions align with business objectives. Effective teamwork ensures seamless data workflows and successful project outcomes. - -**Creativity** allows data scientists to find innovative ways to address complex problems. A creative data scientist might devise a novel approach to handling unstructured data, such as using natural language processing (NLP) techniques to extract insights from customer reviews, providing actionable insights that improve product development. - -These critical soft skills complement technical expertise, making data professionals indispensable contributors to their organizations. - -## 10\. Feature engineering and selection for model optimization - -![Feature engineering and selection](https://assets.roadmap.sh/guest/feature-engineering-1pit4.png) - -For machine learning models to interpret and use any type of data, that data needs to be turned into features. And that is where feature engineering and selection comes into play. These are two critical steps in the data science workflow because they directly influence the performance and accuracy of the models. If you think about it, the better the model understands what data to focus on, the better it'll perform. - -These processes involve creating, selecting, and transforming raw data into useful features loaded with meaning that help represent the underlying problem for the model. - -For example, imagine building a model to predict house prices. Raw data might include information like the size of the house in square meters, the number of rooms, and the year it was built. Through feature engineering, a data scientist could create new features, such as "price per square meter" or "age of the house," which make the data more informative for the model. These features can highlight trends that a model might otherwise miss. - -Feature selection, on the other hand, focuses on optimizing the use and dependency on features by identifying the most relevant ones and removing the redundant or irrelevant features. For example, let's consider a retail scenario where a model is predicting customer churn, here it might benefit from focusing on features like "purchase frequency" and "customer feedback sentiment", while ignoring less impactful ones like "the time of day purchases are made". This helps to avoid the model getting overwhelmed by noise, improving both its efficiency and accuracy. - -If you're looking to improve your data science game, then focusing on feature engineering and selection can definitely have that effect. - -## 11\. Staying Current - -![Staying current with data science trends](https://assets.roadmap.sh/guest/stay-current-vnqqq.png) - -The data science field evolves at an unprecedented pace, driven by advancements in artificial intelligence, machine learning, and data technologies. Staying current with emerging trends is essential for maintaining a competitive edge and excelling in the industry. - -Joining **data science communities**, such as forums or online groups, provides a platform for exchanging ideas, discussing challenges, and learning from peers. For instance, platforms like Kaggle or GitHub allow aspiring data scientists to collaborate on data science projects and gain exposure to real-world applications. - -Attending **data science conferences** is another effective way to stay informed. Events like NeurIPS, Strata Data Conference, or PyData showcase cutting-edge research and practical case studies, offering insights into the latest advancements in machine learning models, big data technologies, and cloud computing tools. - -Engaging in **open-source projects** not only sharpens technical skills but also helps data professionals contribute to the broader data science community. For example, contributing to an open-source MLOps framework might provide invaluable experience in deploying and monitoring machine learning pipelines. - -Embracing trends like **MLOps** for operationalizing machine learning, **AutoML** for automating model selection, and **Generative AI** for creating synthetic data ensures that data scientists remain at the forefront of innovation. These emerging technologies are reshaping the data science field, making continuous learning a non-negotiable aspect of career growth. - -**Summary** - -Mastering these essential data scientist skills—from programming languages and machine learning skills to interpreting data insights and statistical models—will future-proof your [career path in data science](https://roadmap.sh/ai-data-scientist/career-path). These include the core skills of data manipulation, statistical analysis, and data visualization, all of which are central to the data science field. - -In addition, while big data technologies and data engineering skills are not the central focus of a data scientist's role, they serve as valuable, data science-adjacent competencies. Familiarity with big data tools like Apache Spark and cloud computing platforms can enhance scalability and efficiency in handling massive datasets, while data engineering knowledge helps create robust pipelines to support analysis. By building expertise in these areas and maintaining adaptability, you can excel in this dynamic, data-driven industry. - -Check out our [data science roadmap](https://roadmap.sh/ai-data-scientist) next to discover what your potential learning path could look like in this role. - diff --git a/src/data/guides/ai-data-scientist-tools.md b/src/data/guides/ai-data-scientist-tools.md deleted file mode 100644 index 5c803b2e4..000000000 --- a/src/data/guides/ai-data-scientist-tools.md +++ /dev/null @@ -1,313 +0,0 @@ ---- -title: 'Data Science Tools: Our Top 11 Recommendations for @currentYear@' -description: 'Master your data science projects with our top 11 tools for 2025! Discover the best platforms for data analysis, visualization, and machine learning.' -authorId: fernando -excludedBySlug: '/ai-data-scientist/tools' -seo: - title: 'Data Science Tools: Our Top 11 Recommendations for @currentYear@' - description: 'Master your data science projects with our top 11 tools for 2025! Discover the best platforms for data analysis, visualization, and machine learning.' - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-tools-1a9w1.jpg' -isNew: false -type: 'textual' -date: 2025-01-28 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Top data science tools](https://assets.roadmap.sh/guest/data-science-tools-1a9w1.jpg) - -In case you haven't noticed, the data science industry is constantly evolving, potentially even faster than the web industry (which says a lot\!). - -And 2025 is shaping up to be another transformative year for tools and technologies. Whether you're exploring machine learning tools, predictive modeling, data management, or data visualization tools, there's an incredible array of software to enable [data scientists](https://roadmap.sh/ai-data-scientist) to analyze data efficiently, manage data effectively, and communicate insights. - -In this article, we dive into the essential data science tools you need to know for 2025, complete with ratings and expert picks to help you navigate the options. - -## What is Data Science? - -Data science is an interdisciplinary field that combines mathematics, statistics, computer science, and domain expertise to extract meaningful insights from data. It involves collecting, cleaning, and analyzing large datasets to uncover patterns, trends, and actionable information. At its core, data science aims to solve complex problems through data-driven decision-making, using techniques such as machine learning, predictive modeling, and data visualization. - -The data science process typically involves: - -* **Data Collection**: Gathering data from various sources, such as databases, APIs, or real-time sensors. -* **Data Preparation**: Cleaning and transforming raw data into a usable format for analysis. -* **Exploratory Data Analysis (EDA):** Identifying trends, correlations, and outliers within the dataset. -* **Modeling**: Using algorithms and statistical methods to make predictions or classify data. -* **Interpretation and Communication**: Visualizing results and presenting insights to stakeholders in an understandable manner. - -Data science plays a key role in various industries, including healthcare, finance, marketing, and technology, driving innovation and efficiency by leveraging the power of data. - -## Criteria for Ratings - -We rated each of the best data science tools on a 5-star scale based on: - -* **Performance:** How efficiently the tool handles large and complex datasets. This includes speed, resource optimization, and reliability during computation. -* **Scalability:** The ability to scale across big data and multiple datasets. Tools were evaluated on their capability to maintain performance as data sizes grow. -* **Community and Ecosystem:** Availability of resources, support, and integrations. Tools with strong community support and extensive libraries received higher ratings. -* **Learning Curve:** Ease of adoption for new and experienced users. Tools with clear documentation and intuitive interfaces were rated more favorably. - -## Expert Recommendations![Expert Recommendations](https://assets.roadmap.sh/guest/data-science-expert-advice-4lanb.png) - -Picking the best tools for your project is never easy, and it's hard to make an objective decision if you don't have experience with any of them. - -So to make your life a bit easier, here's my personal recommendation, you can take it or leave it, it's up to you, but at least you'll know where to start: - -While each tool has its strengths, my favorite pick among them is **TensorFlow**. Its perfect scores in performance, scalability, and community support (you'll see them in a second in the table below), combined with its relatively moderate learning curve, make it an amazing choice for building advanced neural networks and developing predictive analytics systems. You can do so much with it, like image recognition, natural language processing, and recommendation systems cementing its position as the leading choice (and my personal choice) in 2025\. - -Now, to help you understand and compare the rest of the tools from this guide, the table below summarizes their grades across key criteria: performance, scalability, community support, and learning curve. It also highlights the primary use cases for these tools. - -| Tool | Performance | Scalability | Community | Learning Curve | Best For | -| ----- | ----- | ----- | ----- | ----- | ----- | -| TensorFlow | 5 | 5 | 5 | 4 | Advanced neural networks, predictive analytics | -| Apache Spark | 5 | 5 | 5 | 3 | Distributed analytics, real-time streaming | -| Jupyter Notebooks | 4 | 4 | 5 | 5 | Exploratory analysis, education | -| Julia | 4 | 4 | 4 | 3 | Simulations, statistical modeling | -| NumPy | 4 | 3 | 5 | 5 | Numerical arrays, preprocessing workflows | -| Polars | 5 | 4 | 4 | 4 | Data preprocessing, ETL acceleration | -| Apache Arrow | 5 | 5 | 4 | 3 | Interoperability, streaming analytics | -| Streamlit | 4 | 4 | 5 | 5 | Interactive dashboards, rapid deployment | -| DuckDB | 4 | 4 | 4 | 5 | SQL queries, lightweight warehousing | -| dbt | 4 | 4 | 5 | 4 | SQL transformations, pipeline automation | -| Matplotlib | 4 | 3 | 5 | 4 | Advanced visualizations, publication graphics | - -Let's now deep dive into each of these tools to understand in more detail, why they're in this guide. - -## Data science tools for ML & Deep learning tools - -### TensorFlow - -![TensorFlow](https://assets.roadmap.sh/guest/data-science-tools-tensorflow-exdxy.png) - -TensorFlow remains one of the top data science tools for deep learning models and machine learning applications. Developed by Google, this open-source platform excels in building neural networks, predictive analytics, and natural language processing models. - -* **Performance (★★★★★):** TensorFlow achieves top marks here due to its use of GPU and TPU acceleration, which allows seamless handling of extremely large models. Its ability to train complex networks without compromising on speed solidifies its high-performance ranking. -* **Scalability (★★★★★):** TensorFlow scales from single devices to distributed systems effortlessly, enabling use in both prototyping and full-scale production. -* **Community and Ecosystem (★★★★★):** With an active developer community and comprehensive support, TensorFlow offers unmatched resources and third-party integrations. -* **Learning Curve (★★★★):** While it offers immense power, mastering TensorFlow's advanced features requires time, making it slightly less accessible for beginners compared to simpler frameworks. - -**Strengths:** TensorFlow is a powerhouse for performance and scalability in the world of machine learning. Its GPU and TPU acceleration allow users to train and deploy complex models faster than many competitors. The massive community ensures constant innovation, with frequent updates, robust third-party integrations, and an ever-growing library of resources. The inclusion of TensorFlow Lite and TensorFlow.js makes it versatile for both edge computing and web applications. - -**Best For:** Developing advanced neural networks for image recognition, natural language processing pipelines, building recommendation systems, and creating robust predictive analytics tools for a wide array of industries. - -**Used by:** Google itself uses TensorFlow extensively for tasks like search algorithms, image recognition, and natural language processing. Similarly, Amazon employs TensorFlow to power recommendation systems and optimize demand forecasting. - -## Data science tools for big data processing - -### Apache Spark - -![Apache Spark](https://assets.roadmap.sh/guest/data-science-tools-spark-tjoqm.png) - -An Apache Software Foundation project, Apache Spark is a powerhouse for big data processing, enabling data scientists to perform batch processing and streaming data analysis. It supports a wide range of programming languages, including Python, Scala, and Java, and integrates well with other big data tools like Hadoop and Kafka. - -* **Performance (★★★★★):** Spark excels in processing speed thanks to its in-memory computing capabilities, making it a leader in real-time and batch data processing. -* **Scalability (★★★★★):** Designed for distributed systems, Spark handles petabytes of data with ease, maintaining efficiency across clusters. -* **Community and Ecosystem (★★★★★):** Spark's widespread adoption and integration with tools like Kafka and Hadoop make it a staple for big data workflows. -* **Learning Curve (★★★):** Beginners may find distributed computing concepts challenging, though excellent documentation helps mitigate this. - -**Strengths:** Spark stands out for its lightning-fast processing speed and flexibility. Its in-memory computation ensures minimal delays during large-scale batch or streaming tasks. The compatibility with multiple programming languages and big data tools enhances its integration into diverse tech stacks. - -**Best For:** Executing large-scale data analytics in distributed systems, real-time stream processing for IoT applications, running ETL pipelines, and data mining for insights in industries like finance and healthcare. - -**Used by:** Apache Spark has been adopted by companies like Uber and Shopify. Uber uses Spark for real-time analytics and stream processing, enabling efficient ride-sharing logistics. Shopify relies on Spark to process large volumes of e-commerce data, supporting advanced analytics and business intelligence workflows. - -## Exploratory & Collaborative tools - -### Jupyter Notebooks - -![Jupyter Notebooks](https://assets.roadmap.sh/guest/data-science-tools-jupyter-cgfv4.png) - -Jupyter Notebooks are an essential data science tool for creating interactive and shareable documents that combine code, visualizations, and narrative text. With support for over 40 programming languages, including Python, R, and Julia, Jupyter facilitates collaboration and exploratory data analysis. - -* **Performance (★★★★):** Jupyter is designed for interactivity rather than computational intensity, which makes it highly effective for small to medium-scale projects but less suitable for high-performance tasks. -* **Scalability (★★★★):** While Jupyter itself isn't designed for massive datasets, its compatibility with scalable backends like Apache Spark ensures it remains relevant for larger projects. -* **Community and Ecosystem (★★★★★):** Jupyter's open-source nature and extensive community-driven extensions make it a powerhouse for versatility and support. -* **Learning Curve (★★★★★):** Its simple and intuitive interface makes it one of the most accessible tools for beginners and professionals alike - -**Strengths:** Jupyter's flexibility and ease of use make it indispensable for exploratory analysis and education. Its ability to integrate code, output, and explanatory text in a single interface fosters collaboration and transparency. - -**Best For:** Creating educational tutorials, performing exploratory data analysis, prototyping machine learning models, and sharing reports that integrate code with rich visualizations. - -**Used by:** Jupyter Notebooks have become a staple for exploratory analysis and collaboration. Delivery Hero uses Jupyter to enhance delivery logistics through data analysis and visualization, while Intuit leverages the tool to facilitate financial data analysis in collaborative projects. - -## Data science tools for statistical computing - -### Julia - -![Julia](https://assets.roadmap.sh/guest/data-science-tools-julia-ctqlw.png) - -Julia is an emerging open-source programming language tailored for statistical computing and data manipulation. It combines the performance of low-level languages like C with the simplicity of high-level languages like Python. Julia's strengths lie in its speed for numerical computation and its dynamic type system, making it highly suitable for big data applications and machine learning models. The Julia ecosystem is rapidly growing, offering libraries for data visualization, optimization, and deep learning. - -* **Performance (★★★★):** Julia's design prioritizes speed for numerical and statistical computing, placing it ahead of many high-level languages in terms of raw performance. -* **Scalability (★★★★):** With built-in support for parallel computing, Julia scales well for tasks requiring significant computational power, although its ecosystem is still catching up to Python's. -* **Community and Ecosystem (★★★★):** Julia's growing community and the increasing availability of libraries make it a solid choice, though it's not yet as robust as more established ecosystems. -* **Learning Curve (★★★):** Julia's unique syntax, while designed for simplicity, presents a learning barrier for those transitioning from other languages like Python or R. - -**Strengths:** Julia's ability to execute complex numerical tasks at high speed positions it as a top contender in scientific computing. Its built-in support for parallelism allows it to scale efficiently, while its clear syntax lowers barriers for domain experts transitioning from MATLAB or R. - -**Best For:** Performing advanced statistical analysis, numerical optimization, developing simulations in physics and finance, and implementing machine learning models for high-performance environments. - -**Used by:** The high-performance capabilities of Julia make it a favorite for statistical computing in industries like finance. For example, Capital One uses Julia for risk analytics and modeling, and Aviva employs it to improve actuarial computations and financial modeling processes. - -### NumPy - -![NumPy](https://assets.roadmap.sh/guest/data-science-tools-numpy-nrbc7.png) - -A foundational library in the Python ecosystem, NumPy provides powerful tools for managing data structures, numerical computations, and statistical analysis. It is widely used for data preparation, enabling operations on large multi-dimensional arrays and matrices. - -* **Performance (★★★★):** NumPy's optimized C-based implementation allows it to handle numerical operations with high efficiency, but it relies on integration with other tools for larger or distributed workloads. -* **Scalability (★★★):** As a single-machine library, NumPy is best suited for datasets that fit in memory, though it integrates well with scalable tools like Dask for extended use. -* **Community and Ecosystem (★★★★★):** NumPy's foundational role in Python's data science ecosystem means extensive resources and near-universal compatibility. -* **Learning Curve (★★★★★):** Its straightforward API and clear documentation make NumPy an essential and approachable tool for data preparation and numerical computing. - -**Strengths:** NumPy's versatility and efficiency underpin its widespread adoption in the Python ecosystem. Its array manipulation capabilities—from slicing and reshaping to broadcasting—make it a cornerstone for numerical operations. - -**Best For:** Handling numerical arrays for preprocessing, matrix algebra in physics and engineering, foundational operations for machine learning pipelines, and performing basic statistical analysis efficiently. - -**Used by:** NumPy serves as the foundation for many Python-based workflows. Spotify uses NumPy for numerical computations within its recommendation algorithms, and Airbnb employs it to optimize pricing strategies and improve customer experience through data analysis. - -## **Data science tools for data manipulation & preprocessing tools** - -### Polars - -![Polars](https://assets.roadmap.sh/guest/data-science-tools-polaris-5u7yt.png) - -Polars is a lightning-fast data processing & manipulation library that enables data scientists to handle complex datasets. Unlike traditional libraries, Polars is written in Rust, offering exceptional performance and low memory usage. Its DataFrame API is intuitive and supports multi-threaded operations, making it a strong choice for large-scale data preprocessing and manipulation tasks. - -* **Performance** (★★★★★): Polars' Rust-based architecture ensures exceptional speed and memory efficiency, positioning it as a leading tool for high-performance data manipulation. -* **Scalability** (★★★★): While optimized for larger datasets, its scalability is limited to environments supported by multi-threading rather than distributed systems. -* **Community and Ecosystem** (★★★★): Though its ecosystem is still growing, Polars' strong integration with Python and intuitive API provide a solid foundation. -* **Learning Curve** (★★★★): With a user-friendly interface inspired by Pandas, Polars is easy to adopt for those familiar with similar tools, though Rust concepts may pose challenges for some. - -**Strengths:** Polars stands out due to its unparalleled speed, derived from its Rust-based architecture. Its ability to process data in parallel ensures efficiency even with large datasets, reducing bottlenecks in ETL pipelines. The intuitive API and support for lazy evaluation make it both user-friendly and powerful. - -**Best For:** Processing complex datasets for data cleaning, reshaping large-scale tables, and accelerating ETL pipelines in environments requiring high-speed operations. - -**Used by:** Polars is gaining traction for its exceptional speed in data preprocessing and ETL workflows. Zillow uses Polars for efficient data preprocessing in real estate market analysis, while Stripe adopts it to accelerate ETL processes for handling financial transaction data. - -### Apache Arrow - -![Apache Arrow](https://assets.roadmap.sh/guest/data-science-tools-apache-arrow-6d6mg.png) - -Apache Arrow is revolutionizing how data is stored and transferred for big data applications. Its in-memory columnar format accelerates data processing and integration between multiple datasets and tools. Apache Arrow also acts as a bridge between various programming languages and frameworks, improving the interoperability of data science workflows. - -* **Performance (★★★★★):** Apache Arrow's in-memory columnar format delivers unmatched speed for data processing and transfer between tools. -* **Scalability (★★★★★):** Its design supports seamless scalability across distributed systems, making it ideal for large-scale workflows. -* **Community and Ecosystem (★★★★):** Arrow's adoption by major data tools ensures growing support, though its standalone ecosystem remains limited compared to broader frameworks. -* **Learning Curve (★★★):** Understanding columnar data formats and workflows may require extra effort for beginners but pays off in advanced scenarios. - -**Strengths:** Apache Arrow's columnar format provides a significant boost in performance and compatibility. Its seamless interoperability between tools such as Pandas, Spark, and TensorFlow eliminates data transfer inefficiencies. The library also supports multi-language workflows, making it indispensable for teams leveraging diverse tech stacks. - -**Best For:** Ensuring efficient interoperability between data tools, accelerating data lake operations, and supporting real-time data analytics in distributed systems. - -**Used by:** Google BigQuery integrates Arrow to enhance data interchange and query performance, and AWS Athena relies on Arrow's in-memory format to facilitate faster query responses and real-time analytics. - -## Data science tools for application development - -### Streamlit - -![Streamlit](https://assets.roadmap.sh/guest/data-science-tools-streamlit-iq0sl.png) - -Streamlit is an open-source framework for creating custom data science applications and dashboards. It simplifies the process of building interactive apps by using Python scripts, making it accessible even for those with minimal web development experience. Streamlit's API enables rapid prototyping of machine learning tools and visualizations. - -* **Performance (★★★★):** Optimized for real-time application development, Streamlit is fast for small to medium-scale projects. -* **Scalability (★★★★):** Streamlit scales reasonably well but isn't designed for massive applications. -* **Community and Ecosystem (★★★★★):** Its active community and constant updates ensure excellent support for users. -* **Learning Curve (★★★★★):** With a simple API and Python-centric design, Streamlit is easy for both developers and non-developers. - -**Strengths:** Streamlit's simplicity and speed make it ideal for crafting interactive dashboards with minimal effort. Its integration with popular Python libraries like Pandas and Matplotlib allows users to transform raw data into meaningful insights quickly. - -**Best For:** Creating interactive dashboards for sharing machine learning predictions, visualizing complex datasets with minimal development effort, and rapidly deploying prototypes for stakeholder feedback. - -**Used by:** Streamlit simplifies the creation of interactive dashboards and data applications. Companies like Snowflake use Streamlit to build client-facing data apps, while Octopus Energy employs it to create dashboards that visualize energy consumption data for their customers. - -### DuckDB - -![DuckDB](https://assets.roadmap.sh/guest/data-science-tools-duckdb-iv557.png) - -DuckDB is an open-source analytics database that simplifies structured queries on raw data. Designed to operate within analytical workflows, it supports SQL-based queries without the need for a dedicated database server. Its efficient storage model makes it ideal for querying structured and unstructured data in ad hoc analysis scenarios, making it highly favored for lightweight data warehousing tasks. - -* **Performance (★★★★):** DuckDB delivers impressive speeds for ad hoc analytics, optimized for single-machine workflows. -* **Scalability (★★★★):** Suitable for lightweight to medium-scale tasks, DuckDB integrates well into Python and R environments. -* **Community and Ecosystem (★★★★):** Growing adoption and strong SQL compatibility make it increasingly popular. -* **Learning Curve (★★★★★):** Its SQL-based interface ensures a smooth learning experience for most users. - -**Strengths:** DuckDB's efficiency and ease of use make it a go-to tool for analysts. Its ability to operate without infrastructure overhead allows rapid deployment, and its SQL compatibility ensures accessibility for non-programmers. - -**Best For:** Running interactive SQL queries in development workflows, performing data warehousing tasks without infrastructure overhead, and integrating ad hoc analyses directly into Python-based projects. - -**Used by:** DuckDB's lightweight and efficient SQL analytics have found applications in various industries. MotherDuck integrates DuckDB to enable fast, in-process analytical queries, and SeMI Technologies leverages DuckDB within its Weaviate platform for high-speed vector search analytics. - -### dbt (Data Build Tool) - -![dbt](https://assets.roadmap.sh/guest/data-science-tools-dbt-cozps.png) - -dbt is a development framework for transforming data in warehouses. It allows analysts and engineers to write modular SQL-based transformations and manage data workflows efficiently. With its focus on collaboration and version control, dbt has become an essential tool for teams working on data pipelines. - -* **Performance (★★★★):** dbt's modular approach allows for efficient and scalable SQL transformations. -* **Scalability (★★★★):** Designed for modern data warehouses, dbt handles increasing workloads effectively. -* **Community and Ecosystem (★★★★★):** Its thriving community and vendor support make it indispensable for data pipeline management. -* **Learning Curve (★★★★):** Familiarity with SQL simplifies adoption, though pipeline concepts may require additional learning. - -**Strengths:** dbt's modularity and focus on collaboration streamline complex SQL transformations. Its integration with version control systems ensures reproducibility, while the ability to test and document transformations within the tool fosters better collaboration among data teams. - -**Best For:** Automating SQL transformations for analytics, managing data warehouse workflows with version control, and creating reusable and modular pipelines for team collaboration. - -**Used by:** dbt has become essential for transforming and managing data workflows. JetBlue uses dbt to optimize their data warehouse for improved analytics, and GitLab adopts it to transform raw data into actionable insights, streamlining their analytics operations. - -## Data science tools for data visualization - -### Matplotlib - -![Matplotlib](https://assets.roadmap.sh/guest/data-science-tools-matplotlib-b8lpy.png) - -Matplotlib is a widely used data visualization library in Python that allows users to create static, animated, and interactive visualizations. Known for its flexibility, Matplotlib supports detailed customization, making it suitable for complex visualizations required in data science projects. - -* **Performance (★★★★):** Matplotlib handles visualization tasks efficiently for small to medium datasets but may lag with complex or large-scale rendering. -* **Scalability (★★★):** Designed for single-threaded use, it integrates with scalable tools for extended capabilities. -* **Community and Ecosystem (★★★★★):** A veteran library with vast resources and tutorials ensures comprehensive support. -* **Learning Curve (★★★★):** Accessible for beginners, though mastering advanced features takes effort. - -**Strengths:** Matplotlib's extensive customization options allow it to cater to diverse visualization needs, from simple plots to publication-grade graphics. Its compatibility with libraries like NumPy ensures seamless data integration, while the active community provides extensive tutorials and third-party tools. Despite being a veteran library, Matplotlib remains relevant by adapting to modern visualization demands. - -**Best For:** Creating publication-quality figures, animating time-series data, developing exploratory charts, and embedding visualizations into data-driven applications. - -**Used by:** NASA uses Matplotlib to plot and visualize space mission data, while CERN relies on it for visualizing complex results from particle physics experiments. - -## How to Pick the Right Data Science Tool? - -Choosing the right data science tool can be a daunting task given the vast array of options available. The best tool for your project will depend on several factors, which can be broadly categorized into the evaluation criteria and the context of your specific project. - -### Importance of Evaluation Criteria - -1. **Performance**: This determines how well the tool handles large and complex datasets. Tools that offer fast computation, reliable processing, and efficient use of resources are ideal for high-performance environments. -2. **Scalability**: As data grows, the ability to maintain consistent performance is critical. Tools that scale across big data frameworks or distributed systems ensure longevity and adaptability. -3. **Community and Ecosystem**: A strong community provides valuable resources such as tutorials, documentation, and support. An extensive ecosystem ensures compatibility with other tools and libraries, making integration seamless. -4. **Learning Curve**: A tool's usability can make or break its adoption. Tools with intuitive interfaces and comprehensive documentation enable faster onboarding for teams with diverse expertise. - -### Considering Project Context - -While evaluation criteria provide a standardized way to compare tools, the context of your project ultimately determines the best fit. Key considerations include: - -Tech Stack: The tools should integrate smoothly with your existing technologies and workflows. - -Team Expertise: The skill levels and experience of your team play a significant role in adoption. A tool with a steep learning curve may not be ideal for a team of beginners. - -Project Deadlines: Time constraints can affect the choice of tools. A tool with extensive setup requirements may not suit a project with tight deadlines. - -Data Complexity and Size: The nature and volume of your data should align with the tool's capabilities. - -By balancing these evaluation criteria with the unique needs of your project, you can ensure that the chosen tool maximizes efficiency and effectiveness while minimizing challenges. - -## Final Thoughts - -Data science is an exciting and ever-evolving field, and the tools we've explored here represent the state-of-the-art of innovation for 2025\. Each tool has its own strengths, from high performance and scalability to user-friendly interfaces and robust community support. Whether you're just starting out in data science or managing complex, large-scale projects, there's a tool out there that's just right for you. - -However, choosing that tool isn't just about star ratings or feature lists—it's about finding what works best for your specific context. - -And remember, data science is as much about the journey as it is about the results. Exploring new tools, learning from community resources, and iterating on your processes will make you a better data scientist and help your projects thrive. - -Check out our [data scientist roadmap](https://roadmap.sh/ai-data-scientist) to get a full view of your potential journey ahead\! \ No newline at end of file diff --git a/src/data/guides/ai-data-scientist-vs-ai.md b/src/data/guides/ai-data-scientist-vs-ai.md deleted file mode 100644 index 2248d014e..000000000 --- a/src/data/guides/ai-data-scientist-vs-ai.md +++ /dev/null @@ -1,174 +0,0 @@ ---- -title: "Data Science vs. AI: How I Would Choose My Path" -description: "If you love working with data, should you become a data scientist or an AI engineer? Here's how I made my choice." -authorId: william -excludedBySlug: '/ai-data-scientist/vs-ai' -seo: - title: "Data Science vs. AI: How I Would Choose My Path" - description: "If you love working with data, should you become a data scientist or an AI engineer? Here's how I made my choice." - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-vs-artificial-intelligence-w7gl5.jpg' -isNew: true -type: 'textual' -date: 2025-03-24 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Data Science vs AI: What are the differences?](https://assets.roadmap.sh/guest/data-science-vs-artificial-intelligence-w7gl5.jpg) - -Choosing between specializing in data science and AI comes down to what you like and the problems you want to solve. If you enjoy analyzing data, uncovering insights, and making data-driven decisions, data science might be for you. But artificial intelligence is the way to go if you're into creating systems that can learn, adapt, and make decisions on their own. - -When I first got into tech, I wasn't sure which path to take. I spent late nights researching, feeling both excited and overwhelmed. Data science and AI seemed so similar that telling them apart felt impossible, especially since many jobs required skills in both. But as I dug deeper, I realized they have distinct focuses despite their overlap. - -Data science focuses on extracting meaningful insights from data. As a data scientist, you'll be responsible for designing datasets, building models to analyze data, identifying patterns, and solving complex problems to help businesses make better decisions. You'll do this using statistical analysis and several machine learning techniques like supervised and unsupervised learning. - -In contrast, artificial intelligence is about developing intelligent systems that can perform tasks that typically require human intelligence. These tasks include pattern recognition, predictive analysis, and language translation. You'll design algorithms to learn and solve problems without constant human intervention. - -If you don't know much about data science and AI, don't worry about it. Check out our beginner's guide on [data science](https://roadmap.sh/ai-data-scientist) and [AI](https://roadmap.sh/ai-engineer) to get a good overview of both fields. In this guide, I'll show you what each field is about, the skills you need, and the opportunities they offer. By the end, you'll know which career path suits you best based on your interests, skills, and goals. - -## Data science vs. AI: What are the key differences? - -When discussing data science and AI, many people often mix them up. While they share similarities, their purposes, tools, and techniques differ. The table below summarizes the key differences between data science and AI: - -| Characteristics | Data science | Artificial intelligence | -| ---------------- | ---------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | -| Primary focus | Extracting insights from data | Building intelligent systems that mimic human intelligence to perform tasks | -| Responsibilities | Data collection, data cleaning, data analysis, data modeling, data visualization, and building predictive models | Building and training machine learning models, machine learning algorithms, and artificial intelligence applications | -| Tools | Python, R, SQL, Pandas, Hadoop, TensorFlow, Tableau | Java, Python, PyTorch, OpenCV, spaCy, GPUs, AWS and Keras | -| Educational path | Bachelor's in computer science, statistics, or mathematics; master's in data science | Bachelor's in computer science, mathematics, or engineering; master's in AI or related field | -| Career paths | Data analyst, data scientist, business analyst, machine learning engineer | AI engineer, machine learning engineer, AI product manager, NLP enginner, AI research scientist | -| Applications | Predictive analytics, fraud detection, customer segmentation, healthcare analytics and more | Self-driving cars, chatbots, robotics, predictive maintenance, medical imaging analysis and more | - -## Primary focus - -One big difference between data science and AI is what they focus on the most. Data science focuses on uncovering insights and solving problems through data analytics. It uses structured data like customer databases to identify patterns and solve complex problems. You'll use advanced tools and machine learning techniques like computer vision to work with unstructured data like videos and images. - -If you choose data science, you'll work on extracting valuable insights from data. For example, a data scientist working in finance will use anomaly detection to analyze transactional data. This helps with fraud detection and risk assessment, e.g., unusual login attempts. - -![Data science vs. artificial intelligence: Primary focus](https://assets.roadmap.sh/guest/primary-focus-between-data-science-and-ai-l4fv0.png) - -In contrast, if you choose artificial intelligence, you'll focus on building systems that mimic human intelligence, like learning, reasoning, and solving problems. It uses structured and unstructured data to enable machines to perform tasks like pattern recognition in images and speech and natural language processing. For example, AI virtual assistants like Siri can listen and reply to what you're saying. They use natural language processing models to recognize and understand your voice (human language). It makes it easy for you to ask and get answers to questions just by talking. - -## Daily responsibilities - -Data scientists and AI engineers have different responsibilities that show their focus areas. As a data scientist, your daily duties will include: - -- **Data collection**: This is often the first thing you do when extracting insights from data. Your job will be to collect large volumes of data from several sources, such as databases. For example, a healthcare data scientist will collect and analyze patient data to create models to help in diagnosing diseases. It'll make it easier to predict patient outcomes and make treatment plans better. -- **Data cleaning**: After you collect all the data, the next step is to clean the data. Doing this means removing errors from the raw data to prepare it for data analysis. -- **Data analysis**: After cleaning, you'll analyze data to identify patterns and trends. You'll do this using statistical analysis and machine learning techniques like clustering. -- **Building models**: You'll create predictive models to make future predictions based on historical data. A company like Netflix, for example, uses predictive models to suggest TV shows and movies to you. The data scientist will analyze your past data to see what shows you've watched, how you rated them, and more. Then, they will use this data to build a model that suggests similar shows you'll like. -- **Data visualization**: Visualizing data is about presenting complex data in a visual format. As a data scientist, you'll use data visualization tools like Tableau and Power BI to present data in a way that every team member will understand. - -![Data science vs. artificial intelligence: Job responsibilities](https://assets.roadmap.sh/guest/responsibilities-of-a-data-scientist-and-ai-engineer-wzlzh.png) - -On the flip side, AI involves various responsibilities like designing, training, and testing AI models. As an AI engineer, your daily responsibilities will include: - -- **Data preparation**: You'll collect, clean, analyze, and work with large amounts of data to train and test AI models (i.e., machine learning models). Yes, as an AI engineer, you'll also analyze data, although it is the primary duty of the data scientists. Doing this lets you check if the data is good for the AI model and spot any problems that could mess up the training. -- **Model training**: You'll be responsible for developing algorithms that enable machines to learn from data using machine learning techniques like computer vision and neural networks. For example, automotive industries use artificial intelligence to create self-driving cars. They use AI algorithms to analyze sensor data from cameras, radar, and lasers to see what's around them. -- **Review models**: You'll test and make sure that the models provide reliable results. Imagine you've developed a model to identify diseases from analyzing medical images. You'll use images the model hasn't seen before to test its accuracy at spotting diseases. If it misses something, that's your cue to tweak it until it gets better. -- **Implementation**: Once you create a model, you need to make sure it works well with other systems. It's like having a chatbot on a website to handle customer inquiries, using a fraud detection system in a bank to analyze transactions, or incorporating GPS functionality in self-driving cars for navigation. Additionally, you'll monitor and maintain its performance and update it when needed. - -## The tools you'll use - -One important thing to consider when choosing between data science and AI is the tools you'll use. In data science, you'll work with programming languages like [Python](https://roadmap.sh/python), R, and [SQL](https://roadmap.sh/sql) to extract insights and analyze data. You'll use Python libraries like Pandas to manipulate data and NumPy to make math calculations. - -As a data scientist, you'll also use big data technologies like Hadoop to work with large volumes of data. Machine learning libraries like TensorFlow will help you develop advanced machine learning models. Also, you'll use data visualization tools like Tableau and Power BI when sharing your results. - -![Data science vs. artificial intelligence: tools](https://assets.roadmap.sh/guest/tools-for-data-science-and-artificial-intelligence-q6i4v.png) - -On the flip side, artificial intelligence needs more advanced tools to work its magic. You'll use programming languages like [Java](https://roadmap.sh/java) and [Python](https://roadmap.sh/python), like in data science, but you'll focus on AI frameworks like PyTorch to train deep learning models, especially neural networks. These frameworks can do various machine learning tasks, such as supervised learning (e.g., image classification), unsupervised learning (e.g., anomaly detection), and reinforcement learning (e.g., game playing) when needed. - -Tools like OpenCV will help with computer vision tasks, and libraries like NLTK and spaCy will help with natural language processing. As an AI engineer, you'll also work with specialized hardware like graphics processing units (GPUs) and tensor processing units (TPUs). These tools provide the computational power to train large neural networks well. Finally, you'll work with cloud platforms like [AWS](https://roadmap.sh/aws) and Google Cloud AI Platform to deploy and manage your AI models. - -## How do the skills you need differ? - -Now that you know the differences between data science and artificial intelligence, let's look at the skills you need. As a data scientist, it's important to build a solid understanding in the following areas: - -- Proficient in programming skills like [Python](https://roadmap.sh/python) and [SQL](https://roadmap.sh/sql) for data analysis and manipulation. -- Strong understanding of statistics and mathematics to analyze and interpret data. -- Good in data wrangling for cleaning and organizing datasets before analysis. -- Proficient in data mining to extract useful patterns and insights from large datasets. -- Strong data visualization techniques to present valuable insights through tools such as Matplotlib or Tableau. -- Basic understanding of machine learning principles and algorithms to apply predictive analytics and solve business problems. -- Good critical thinking skills to analyze data and provide solutions to business problems using data. - -![Skills for data science and artificial intelligence](https://assets.roadmap.sh/guest/important-skills-for-data-science-and-ai-sj2nh.png) - -In contrast, as an AI engineer, you'll create complex AI systems using the following skills: - -- In-depth knowledge of advanced machine learning concepts, including neural networks, to create models for tasks like image recognition or natural language processing. -- Proficiency in programming skills ([Python](https://roadmap.sh/python), [C++](https://roadmap.sh/c-plus-plus), [Java](https://roadmap.sh/java)) for creating complex algorithms. -- Understanding mathematics, e.g., linear algebra and calculus for algorithm development. -- Experience with frameworks like TensorFlow and PyTorch for building and deploying AI models. -- Knowledge of computer vision and natural language processing to create intelligent systems that understand and interact with data. - -## The educational pathway to data science and artificial intelligence career - -A very good educational foundation is necessary to start a career as a data scientist. The first step is getting your bachelor's degree in computer science, data science, or artificial intelligence. These fields cover topics like algorithms, machine learning, and software development. - -![Data science vs. artificial intelligence: Educational path](https://assets.roadmap.sh/guest/educational-pathway-to-data-science-and-ai-careers-clcgu.png) - -On the other hand, artificial intelligence often requires higher educational qualifications, such as a bachelor's, master's, or Ph.D., due to its technically complex and research-oriented nature. You'll first need a bachelor's degree in computer science, data science, or artificial intelligence to start a career in this field. This will give you a good understanding of machine learning, mathematics, and statistics. You'll go into detail on topics like algebra and calculus and learn languages like [Python](https://roadmap.sh/python) and [Java](https://roadmap.sh/java). Some AI engineers may pursue a master's degree if they aim for senior or research positions, but it's not always necessary. - -Also, you don't always have to attend a university to learn. Some people who want to become data scientists and AI engineers go to boot camps, take online courses, or get certifications instead. Platforms like [roadmap.sh](https://roadmap.sh) offer detailed [data science](https://roadmap.sh/ai-data-scientist) and [AI courses](https://roadmap.sh/ai-engineer) to help you learn. The platforms are more flexible and focus on practical projects, so you can create a portfolio to showcase your skills. - -## Which field offers better career prospects? - -Data science and artificial intelligence both look promising in terms of job opportunities. Both fields are in high demand, so there are plenty of job opportunities out there for you. However, artificial intelligence jobs usually pay better because they need more advanced skills and technical knowledge in machine learning and algorithm development. - -![Data science vs. artificial intelligence: career path](https://assets.roadmap.sh/guest/career-paths-and-industries-for-data-science-and-ai-i333u.png) - -Data science is a recognized field with many uses in industries like finance, healthcare, and marketing. If you're interested in data science, you could work as a [data analyst](https://roadmap.sh/data-analyst), business analyst, or data scientist. According to [Indeed](https://www.indeed.com/career/data-scientist/salaries?from=top_sb), the average salary for a data scientist in the United States (USA) across all industries is $125,495. - -![Average salary of a data scientist in USA](https://assets.roadmap.sh/guest/average-salary-of-a-data-scientist-in-usa-vevcq.png) - -On the other hand, artificial intelligence is in high demand in industries like robotics, automotive, and tech startups. If you're interested in artificial intelligence, you can work as a machine learning or AI engineer. [According to Indeed](https://www.indeed.com/career/machine-learning-engineer/salaries?from=top_sb), engineers working in machine learning in the USA make an average of $162,735 annually. - -![Average salary of an engineer in machine learning](https://assets.roadmap.sh/guest/average-salary-of-an-engineer-in-machine-learning-ncqpb.png) - -While salary is important when choosing between data science and artificial intelligence, it shouldn't be your only factor. For me, it's less about which field pays more and more about what excites you daily. You'll enjoy success and work satisfaction when you choose a job that matches your interests. - -## Do data science and artificial intelligence overlap? - -Yes, data science and artificial intelligence overlap. Data science is like the backbone of AI. It supplies the data and insights needed to train and improve artificial intelligence models. - -Many skills overlap between data science and artificial intelligence. For instance, being good at coding in [Python](https://roadmap.sh/python), R, and [SQL](https://roadmap.sh/sql) is important in both fields. Both fields also need machine learning, statistics, and data visualization knowledge. Switching from data science to AI or vice versa is straightforward because they share many similarities in skills and tools. - -## Will AI replace data science? - -Will AI replace data science? This is a common concern among many data scientists since there's been a lot of buzz about AI. The simple answer is no: AI is not replacing data science. - -Artificial intelligence tools can make it easier for data scientists to analyze data and predict trends faster. But we humans make sense of all that data, put it into context, and use it to make decisions for the company. Instead of seeing AI as a rival, think of it as a helpful tool that can help you get better at data science. - -## Next steps to find your fit - -If you're still trying to figure out which path to take, here are a few steps to help you find what feels right for you: - -### Know your strengths - -To find out which path is right for you, consider what you're good at and what excites you. Data science might be a good fit if you love analyzing data and finding out hidden stories behind numbers. - -On the other hand, artificial intelligence might be better if you're into technology, algorithms, and creating systems that imitate human intelligence. - -### Connect with others - -Talk to some data science and artificial intelligence professionals and get their advice. Start by attending conferences or joining [online communities](https://roadmap.sh/discord) to connect with others. - -You can ask questions, share your thoughts, and learn from others as a community member. When you chat with other professionals, ask them what they enjoy about their work, what their challenges are, and if they have any tips for newcomers. Listening to their stories can give you a better idea of what each job is really like. - -### Don't be afraid to try - -Don't be afraid to try new things and step out of your comfort zone. You might think you're into data science, but after taking some classes or working on projects, you could realize that artificial intelligence is where your true passion lies. It is completely normal and is part of the learning journey. - -## What next? - -Artificial intelligence and data science have changed how businesses work and make decisions. Both fields have great job opportunities, but the skills needed for each are different. Understanding the basics of these fields will help you pick the right path for you. - -It's not about figuring out which field is better than the other—it's about choosing the one that fits your goals, interests, and skills. So relax and take some time to figure out what you're really into before making any big decisions. - -For continuous learning, visit the roadmap.sh [data science](https://roadmap.sh/ai-data-scientist) and [artificial intelligence](https://roadmap.sh/ai-engineer) guides. Both guides have all the resources you need to start your journey as a data scientist or AI engineer. diff --git a/src/data/guides/ai-data-scientist-vs-business-analytics.md b/src/data/guides/ai-data-scientist-vs-business-analytics.md deleted file mode 100644 index e863bfa16..000000000 --- a/src/data/guides/ai-data-scientist-vs-business-analytics.md +++ /dev/null @@ -1,189 +0,0 @@ ---- -title: "Data Science vs Business Analytics: How I'd Choose My Path" -description: "I once struggled to choose between data science and business analytics. If you're in the same boat, I'll guide you to find the right fit." -authorId: ekene -excludedBySlug: '/ai-data-scientist/vs-business-analytics' -seo: - title: "Data Science vs Business Analytics: How I'd Choose My Path" - description: "I once struggled to choose between data science and business analytics. If you're in the same boat, I'll guide you to find the right fit." - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-and-business-analytics-zxubk.jpg' -isNew: true -type: 'textual' -date: 2025-03-24 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Data science vs business analytics comparison](https://assets.roadmap.sh/guest/data-science-and-business-analytics-zxubk.jpg) - -If you enjoy coding, working with algorithms, and solving mathematical problems, you'll likely thrive in [data science](https://roadmap.sh/ai-data-scientist). However, if you prefer analyzing trends, making strategic decisions, and communicating insights, business analytics is a better fit. - -When I was deciding between the two, I kept getting lost in job descriptions and salary comparisons. But what really mattered was how I wanted to work with data. Did I want to build models and predict future trends? Or did I want to interpret existing data to solve business problems? - -If you're facing the same dilemma, the best way to decide is by understanding what each role actually does and how it fits your strengths. In this guide, I'll walk you through the key differences, career paths, and real-world examples, so by the end, you'll have clarity on the right choice for you. - -Before diving in, here's a quick comparison: - -| | Data Science | Business Analytics | -| ------------ | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| Key role | Building data models and using math and programming to analyze complex data and extract insights. | Analyzing data and digging for insights to solve specific business problems and presenting them to inform strategy. | -| Skills | Machine learning, advanced mathematics, programming, business intelligence, data analytics, and data visualization. | Data analysis using Excel or Sheets, SQL, visualization, basic statistical modeling, business intelligence, communication, presentation, and problem-solving. | -| Tools | Python, R, SQL, Tableau, PyTorch, Power BI, Tensorflow. | SQL, Excel, business acumen, data visualization tools, and communication. | -| Career paths | Data scientist, AI researcher, data engineer, machine learning engineer. | Business analyst, data scientist, operation analyst, data analyst, product analyst, marketing analyst. | - -Now, let’s get into the meat. If I were in your shoes, the first step I'd take is to understand the differences and similarities between business analytics and data science. - -## Data science vs. business analytics: What sets them apart? - -The difference between data science and business analytics comes down to how you want to work with data. - -When I was making this decision, I had to ask myself: Do I enjoy coding, working with algorithms, and solving mathematical problems? If so, data science was the better fit. Or do I prefer analyzing trends, making strategic decisions, and communicating insights? In that case, business analytics made more sense. - -If you go the data science route, you'll take a highly technical approach that blends [computer science](https://roadmap.sh/computer-science), mathematics, and business knowledge. You'll work with business analysts to understand key challenges, then clean, explore, and mine unstructured and structured data to improve its quality. - -Once the data is ready, you'll build and test predictive models using machine learning algorithms to uncover hidden patterns. These models help businesses make data-driven predictions, optimize processes, and automate decision-making. - -When I looked at these responsibilities, I realized data science was all about solving complex problems, discovering trends, and forecasting what's next. If that excites you, it might be the right path. - -![Data science vs business analytics: what sets them apart?](https://assets.roadmap.sh/guest/data-science-vs-business-analytics-what-sets-them-apart-ineqp.png) - -Business analytics might be a better fit if you don't fancy data science. You'll sit at the intersection of business, data, and operations. You are responsible for evaluating the business's overall health, identifying gaps, and recommending solutions to improve business operations. - -You'll also perform basic data cleaning and transformation using tools like [SQL](https://roadmap.sh/sql) and Excel. However, your primary focus is analyzing data to uncover insights, create reports, and present findings to stakeholders. - -Beyond analysis, I realized that business analysts also take action. You'll recommend strategic next steps based on data insights and collaborate with decision-makers to execute them. This means being involved in change strategy and ensuring that insights lead to tangible business improvements. - -For example, imagine you work for an energy company and notice a drop in residential electricity usage due to increased solar panel adoption. A data scientist will build predictive models to forecast energy demand, optimize distribution, and detect anomalies. A business analyst, meanwhile, will interpret these insights, develop strategies like solar buyback programs, and lead their implementation to drive impact. - -## What Are Their Similarities? - -Although data science and business analytics have differences, after working in both fields, I realized that they share the same goal. Both disciplines focus on transforming raw data into valuable insights that drive business decisions. - -![Data science vs business analytics: what connects them?](https://assets.roadmap.sh/guest/data-science-vs-business-analytics-what-connects-them-z10j8.png) - -Think of them as two sides of the same coin. Both fields involve data mining, statistical analysis, data visualization, [SQL](https://roadmap.sh/sql) usage, problem-solving, and stakeholder collaboration. They work together to help businesses make sense of their data, but their approach and execution set them apart. - -The next step to take is to understand what daily tasks may look like and which one you’d prefer. - -## What Would Your Day-to-Day Look Like? - -Your day-to-day life as a data scientist or business analyst depends on the size of your company and team, industry, and project focus. I’ll break down the primary responsibilities of both fields to give you a clearer picture. - -What are the key responsibilities of a data scientist? -A data scientist analyzes complex data to extract insights, build predictive models, and support data-driven decision-making. - -**Primary tasks:** - -- **Business understanding**: Understand the why behind what you're building. You'll often work with stakeholders to define project and business requirements that guide data usage and model development. -- **Data ingestion**: Gather raw data from systems. This data is obtained from databases, APIs, IoT sensors, Excel sheets, etc. A data scientist collects all the relevant data needed to solve the current business problem. -- **Data processing, migration, and storage**: You'll spend most of your time cleaning, transforming, and migrating structured and unstructured raw data. The goal is to convert data into suitable formats and structures for analysis and accessibility. -- **Data analysis**: Data scientists identify patterns in data behavior using visualization tools and statistical techniques such as Bayesian Inference, A/B testing, and K-means clustering. -- **Building machine learning models**: You'll define the appropriate modeling approach based on business objectives, data characteristics, and analytical requirements. Then, you'll build, train, validate, and fine-tune the model using historical data to predict future trends or automate workflows. Depending on the use case, you may also leverage pre-trained models or transfer learning for faster deployment. -- **Deploying and testing predictive models**: You'll test your model against business requirements and deploy it to production. -- **Reporting**: You also need to present model results to stakeholders using visualizations and clear, concise explanations. - -Data scientists collaborate closely with data engineers to build data extraction and transformation pipelines. You'll also work with business analysts and other stakeholders to set business requirements and align the model with them. - -You'd perform these responsibilities using tools like TensorFlow, Pandas, Jupyter Notebooks, Scikit-learn, Apache Spark, Hadoop, [Docker](https://roadmap.sh/docker), [GitHub/Git](https://roadmap.sh/git-github), SQL, Tableau, and cloud platforms ([AWS],(https://roadmap.sh/aws) Google Cloud, Azure) - -### What Are the Key Responsibilities of a Business Analyst? - -As a business analyst, you'll bridge the gap between business needs and technical solutions by analyzing data and processes and using these insights to drive strategic decisions. Your primary tasks involve: - -- **Data manipulation and analysis**: While business analysts don't need to be expert programmers, you need SQL and Excel for querying databases and lightly analyzing unstructured and structured data. Some business analysts also pick up Python for deeper analysis. -- **Business acumen**: You'd need to know how to perform holistic business analysis. This involves asking the right questions: How can I solve business problems with data? What processes benefit from data-driven insights? Do I have the right data? How can I start collecting this data? Understanding how different departments (marketing, sales, finance) operate is necessary to align data insights with business goals. -- **Data visualization and reporting**: You'll create interactive dashboards and reports using visualization tools like Tableau, Power BI, and Google Data Studio. -- **Communication and stakeholder management**: You'll be involved in clearly communicating complex data insights and providing simple and actionable business recommendations. You'll often present findings to executives who may have little or no technical skills. - -As a business analyst, you'll perform these responsibilities using Tableau, Power BI, Salesforce, Excel, Google Analytics, [SQL](https://roadmap.sh/sql), Microsoft Power BI, Google Sheets, and Looker. - -![Tools for data science and business analytics](https://assets.roadmap.sh/guest/tools-for-data-science-and-business-anaytics-s7iwy.png) - -## Career Prospects: What Paths Are Available to You? - -When choosing a path, I found it helpful to learn about the diverse and promising career options in each field. - -![Career paths for data science and business analytics](https://assets.roadmap.sh/guest/career-paths-for-data-science-and-business-analytics-n0auj.png) - -### Data Science Career Paths - -If data science is your focus, you have several options, but I'll focus on the three leading fields: data science, machine learning engineering, and artificial intelligence. - -#### Data Scientist - -This role involves developing machine learning algorithms, analyzing large datasets, and extracting actionable insights to support business decision-making. You'll work with structured and unstructured data, applying statistical methods and algorithms to uncover patterns, predict trends, and solve complex problems. - -According to [Indeed](https://www.indeed.com/career/data-scientist/salaries), the average annual salary for a data scientist in the U.S. is $125,639, with a range between $79,587 and $198,339. - -![Data scientist salary in United States](https://assets.roadmap.sh/guest/data-scientist-salary-in-united-states-1ks00.png) - -#### Machine Learning Engineer - -Machine learning engineers design, optimize, and deploy machine learning algorithms in production environments. Unlike data scientists, you'll specialize in software engineering, ensuring models are scalable, efficient, and seamlessly integrated into real-world applications. - -According to [Indeed](https://www.indeed.com/career/machine-learning-engineer/salaries?), Machine Learning Engineers earn an average salary of $163,390 per year in the U.S. - -![Machine learning engineer salary in United States](https://assets.roadmap.sh/guest/machine-learning-engineer-salary-in-united-state-8s0tv.png) - -#### AI Specialist - -AI specialists develop AI-driven solutions, lead artificial intelligence research, and manage business initiatives. - -According to [Glassdoor](https://www.glassdoor.co.uk/Salaries/ai-specialist-salary-SRCH_KO0%2C13.htm?countryRedirect=true), the average salary for an AI Specialist in the U.S. is $134,500 per year. - -![AI specialist salary](https://assets.roadmap.sh/guest/ai-specialist-salary-7y3wh.png) - -A computer science or mathematics or data science/AI master's degree or PhD is often desired for data science career paths. - -### Business Analytics Career Paths - -If you choose business analytics instead, several career paths are available, but I'll discuss three leading ones: business analyst, business intelligence analyst, and operations analyst. - -#### Business Analysts - -In this role, you'll get to interpret data, identify business trends, and recommend strategies to optimize performance. You'll also work closely with stakeholders to assess business challenges and use data to drive process improvements and cost-saving measures. - -According to [Indeed](https://www.indeed.com/career/business-analyst/salaries?), the average salary for a business analyst in the U.S. is $85,000 per year, with potential earnings exceeding $100,000 for senior-level professionals. - -![Business analyst salary in United States](https://assets.roadmap.sh/guest/business-analyst-salary-in-united-state-qxei8.png) - -#### Business Intelligence Analysts - -This role focuses on data visualization, reporting, and trend analysis. Working in this role involves developing dashboards, creating reports, and helping organizations monitor performance metrics in real-time. - -According to [Indeed](https://www.indeed.com/career/business-intelligence-analyst/salaries?), Business intelligence analysts in the U.S. earn an average salary of $97,872 per year, with top earners making over $130,000. - -![Business intelligence analyst salary in United States](https://assets.roadmap.sh/guest/business-inntelligence-analyst-salary-in-united-states-nwx5v.png) - -#### Operations Analysts - -Operations analysts focus on optimizing business workflows, improving efficiency, and reducing operational costs through data-driven analysis. This role is common in industries like logistics, finance, and retail, ensuring processes run smoothly and profitably. - -According to [Indeed](https://www.indeed.com/career/operations-analyst/salaries?), the average salary for an operations analyst in the U.S. is $74,648 per year, with potential earnings exceeding $100,000 in industries like finance and technology. - -![Operations analyst salary in United States](https://assets.roadmap.sh/guest/operations-analyst-salary-in-united-states-neddf.png) - -Business analytics admission requirements for those interested in this field often include a bachelor's degree in business, economics, mathematics, or a related field, along with proficiency in statistics, data interpretation, and business intelligence tools. - -## Why Choose One Over the Other? - -I chose data science because I have loved mathematics since my early school years, and anything AI gives me life. - -As I mentioned earlier, if you enjoy coding, solving technical and mathematical problems, and developing data-driven solutions, data science might be the right path for you—especially if AI, machine learning, and big data interest you. This field focuses on building models, algorithms, and predictive systems to extract meaningful insights from data. - -On the other hand, if you prefer interpreting data, identifying trends, and using insights to drive strategic business decisions, business analytics is a better fit. This path is ideal for those who enjoy working closely with stakeholders to solve real-world business problems through data-driven strategies. - -If you're still unsure, experimenting with small projects in both fields can help you determine which excites you more. Sometimes, hands-on experience is the best way to find the right path. Also, some business analytics programs focus on ML, allowing you to explore both fields simultaneously. - -## Next Steps? - -Once you've chosen between data science and business analytics, the best thing you can do is stop second-guessing and start learning. Follow our [Data Scientist Roadmap](https://roadmap.sh/ai-data-scientist) as your step-by-step guide from beginner to expert, tracking your progress along the way. - -You can also start with [Python](https://roadmap.sh/python), explore data analysis, and learn machine learning basics. But, if your preference is business analytics, master Excel, learn visualization tools like Tableau or Looker, and practice creating dashboards. - -The roadmap also helps you schedule learning time and block study time on your calendar to stay consistent. For a detailed overview of any specific role, join the [Discord community](https://roadmap.sh/discord) and stay informed! diff --git a/src/data/guides/ai-data-scientist-vs-computer-science.md b/src/data/guides/ai-data-scientist-vs-computer-science.md deleted file mode 100644 index d956d095f..000000000 --- a/src/data/guides/ai-data-scientist-vs-computer-science.md +++ /dev/null @@ -1,166 +0,0 @@ ---- -title: 'Data Science vs. Computer Science: Which Path to Choose' -description: 'Data science or computer science? Learn the tools, roles, and paths in each field to decide which fits your strengths and career goals.' -authorId: ekene -excludedBySlug: '/ai-data-scientist/vs-computer-science' -seo: - title: 'Data Science vs. Computer Science: Which Path to Choose' - description: 'Data science or computer science? Learn the tools, roles, and paths in each field to decide which fits your strengths and career goals.' - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-vs-computer-science-rudoc.jpg' -isNew: false -type: 'textual' -date: 2025-02-06 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Data science vs computer science comparison?](https://assets.roadmap.sh/guest/data-science-vs-computer-science-rudoc.jpg) - -If you love uncovering patterns in data and using those insights to solve real-world problems, [data science](https://roadmap.sh/ai-data-scientist) might be the right fit for you. On the other hand, if you're drawn to creating systems, writing code, and building the tools that power today's technology, [computer science](https://roadmap.sh/computer-science) could be your path. - -From my experience working on projects in both fields, I've seen how they overlap and where they differ. As a data scientist, you'll focus on analyzing complex data using math, programming, and problem-solving skills. Your work might include building models to extract meaningful insights from data, enabling you to identify patterns, predict trends, detect fraud, or improve recommendations. - -Computer science, in contrast, focuses on understanding how computing systems work. You'll write code, design algorithms, and develop programs to solve problems. You might create web applications and software tools or even dive into artificial intelligence and cybersecurity. - -Although these fields overlap in areas like programming, they cater to different interests and career goals. In this guide, I'll walk you through what each field involves, the skills you need, and the opportunities they offer. By the end, you'll have the clarity to make an informed choice about your future based on which path suits you best. - -## Data science vs. computer science: What are the key differences? - -First, let's look at this table that summarizes the differences. Then, we'll discuss each one in more detail. - -| Characteristics | Data science | Computer science | -| ----------------------------- | -------------------------------------------------------------------------- | -------------------------------------------------------------------------- | -| Primary focus | Extracting insights from data | Building computer systems to perform tasks | -| Industries | Finance, healthcare, marketing, e-commerce | cybersecurity, gaming, education | -| Career paths | Data analysts, data scientists, machine learning engineers, data engineers | Software engineers, computer scientists, cybersecurity analyst | -| Key areas and specializations | Machine learning, data mining, data visualization, statistical analysis | Data structures, software development, artificial intelligence | -| Tools and technologies | Python (Pandas, NumPy), R, SQL, TensorFlow | Python, Java, C++, JavaScript, Git, Vs code, databases (MySQL and MongoDB) | -| Educational background | Mathematics, economics, computer science, data science, physics | computer science, mathematics, electrical engineering | - -**Your primary focus** -The big difference between data science and computer science is their primary focus. If you choose data science, your role will involve extracting insights and helpful information from datasets. You'll use different tools and techniques to interpret and analyze data. The aim is to help businesses make data-driven decisions. Netflix, for example, uses machine learning and data science to learn your viewing history. Data scientists then analyze data to predict what shows you'll like and suggest movies. - -In contrast, if you choose computer science, you'll focus on building computer systems to perform tasks. You'll create tools that make computers, web apps, data analysis, etc, work better. For example, a company like Google uses computer science to create algorithms that make search engines work. Computer scientists then create software systems and algorithms that will give you accurate results when you search online. - -**Your career path** -Computer science and data science have very different career paths. As a data science professional, you'll use data to help make decisions and solve business problems. You'll work in various industries, like finance and healthcare, and take on roles such as: - -- **Data analyst**: Collect and analyze data to help businesses make intelligent decisions. For example, as a data analyst in healthcare, you would use data analysis to know which treatments work best for a specific illness. -- **Data scientists**: Build predictive models to predict outcomes. As a data scientist in finance, you'll use data analysis to forecast how the stock market might go. -- **Data engineers**: Build and maintain data systems that enable data analysis. -- **Machine learning engineer**: Design and build machine learning models to solve everyday problems. As a machine learning engineer at a robotic car company, you'll create models to spot people, roads, traffic lights, and other cars. You'll be responsible for designing machine learning algorithms to help the car make fast decisions. - -![Data science vs. computer science: Career paths](https://assets.roadmap.sh/guest/career-paths-and-industries-for-data-science-and-computer-science-0tzlq.png) - -In contrast, as a computer science professional, you'll build the tools data engineers use to get work done. You'll also work in different industries, such as cybersecurity and gaming, and take on roles like: - -- **Software engineers**: Build and maintain software systems, including gaming platforms like Steam. -- **Computer scientists:** Study computer systems' theoretical and practical aspects to create applications. -- **Cybersecurity analyst**: Monitor and address possible security threats like data breaches. - -**Your key areas** **of** **specialization** -Data science focuses on machine learning, data mining, statistical analysis, and more**.** As a data scientist, you'll use these specializations to understand trends and make better decisions. - -In contrast, computer science focuses on artificial intelligence, [data structures](https://roadmap.sh/datastructures-and-algorithms), and software development. As a computer scientist, you'll study these fields to create the tools that data scientists use. - -**The** **tools and technologies** **you'll** **use** -Choosing data science requires using different tools and technologies to manipulate data. These tools include machine learning libraries **(TensorFlow)** and languages like [**Python**](https://roadmap.sh/python) **and R**. Python libraries like Pandas will help you with data manipulation and NumPy for math calculations. As a data scientist, you'll also use big data technologies like Hadoop to work with huge amounts of data. - -On the other hand, computer science focuses on software development. As a computer scientist, you'll use programming languages like [Python](https://roadmap.sh/python), [C++](https://roadmap.sh/cpp), and [JavaScript](https://roadmap.sh/javascript) to create different web applications. You'll also use tools like [React](https://roadmap.sh/react), [Git](https://roadmap.sh/git-github), databases **(MySQL and** [**MongoDB**](https://roadmap.sh/mongodb)**)**, and IDEs **(VS code)** to write and test codes. - -## What education do you need for computer and data science careers? - -Getting a computer science degree gives you a solid foundation for software development. It will help you understand computer science principles and how to code and solve issues. Examples of these principles include programming languages, data structures, and operating systems. As a computer science graduate, you'll have a strong foundation that will help you land various tech jobs. - -![Data science vs. computer science: Educational background](https://assets.roadmap.sh/guest/what-education-do-you-need-for-a-computer-and-data-science-career-xbujx.png) - -Many universities offer specializations in data science within their computer science programs. Others treat data science as a separate program, recognizing it as a field in its own right. They teach you all about machine learning, data visualization, statistics, and more. These data science programs combine ideas from computer science, mathematics, and other fields. At the end of the program, you'll get a data science degree and the necessary skills to get a tech job. - -Studying at a university is not the only way to get a data and computer science education. Many computer science students and aspiring data scientists attend boot camps and learn via certifications or online tutorials. For instance, you can find many [data science courses](https://roadmap.sh/ai-data-scientist) on [roadmap.sh](http://roadmap.sh) and learn at your own pace. - -Learning this way is more flexible and can work with all kinds of schedules and ways of learning. For instance, you can juggle work and study at the same time, which is much harder to do when you study at a university. If you go for a traditional degree like a data science degree, you'll need to invest more time, but you'll get a broad education. - -With these educational paths in mind, a common question arises: **D\*\***o you need a computer science degree to pursue a career in data science?\*\* The answer is a simple no. - -A computer science degree is not always required for computer and data science positions. It can help you start your career in data science, for example, by giving you a solid programming foundation. However, you can study other fields like mathematics, physics, and economics and still be a successful data scientist. You can also go through boot camps and online tutorials on data analysis, machine learning, and data visualization to gain the necessary skills. - -Also, having some practical skills and constant practicing will give you more experience. When practicing, work on personal and open-source projects and build your portfolio to increase your chances of getting a job. Create time to attend meetups and [join online communities](https://discord.com/invite/cJpEt5Qbwa) to chat with other professionals. - -## What are the essential skills you'll need? - -Computer science and data science have a broad range of specialized skill sets. Some of these skills are relevant in both fields, and others are unique. - -![Computer science and data science skills](https://assets.roadmap.sh/guest/essential-skills-for-computer-science-and-data-science-k8p58.png) - -Even though computer science and data science are not the same, they do have some skills in common. These shared skills make it easy to switch between the two fields. Yes, it's true; you can transition from computer to data science and vice versa. The following are some examples of these shared skills: - -- **Programming skills**: Programming skills are a crucial common ground for both fields. Knowing how to code to solve problems as a computer or data scientist is important. The process involves learning programming languages like [Python](https://roadmap.sh/python), having a deep understanding of data structures, and more. It lets you do software development **(computer science)** or data manipulation **(data science)**. However, it is worth noting that some tasks, like data visualization, do not require coding. -- **Solving problems:** As a computer or data scientist, it is important to be able to solve problems. This helps you to create software, fix errors, and understand data. -- **Mathematics and statistics**: Knowledge of mathematics and statistics will help you to solve problems in both fields. Computer science uses math principles in areas like algorithms and data structures. They will help you as a computer scientist make fast and better software and solve coding issues. As a data scientist, you use statistics to analyze data and machine learning. - -### What skills do you need for computer science? - -- **Programming languages:** Computer scientists use programming languages to give instructions to computers**.** Knowing one or more of these programming languages **(JavaScript, Java, etc.)** will help you to be successful in this field. -- **System architecture:** Knowledge of system architecture will help you build reliable computer systems**.** -- **Software development methodologies:** Software methodologies help you to plan and manage software projects. These methodologies **(agile, scrum, etc.)** will help you collaborate better with others when creating software. - -### What skills do you need for data science? - -- **Machine learning techniques**: Machine learning techniques are important skills in data science. A deep understanding of machine learning techniques will help you build prediction models. Among the many examples of these techniques are clustering and decision trees. They allow computers to make predictions and recognize patterns without instructions from anyone. -- **Data analytics:** To get into the data science field, you must know data analytics. It is the starting point for many data science tasks, e.g., building machine learning models. Data analytics allows you to understand data, find patterns, and draw conclusions. -- **Data visualization techniques**: These help present data results in clear visual stories. As a data scientist, they allow you to show patterns that might be hard to see in raw numbers in pictures or graphs. You do this using tools like Tableau, Matplotlib, or Power BI. Some examples of these techniques include bar charts, histograms, and scatter plots. - -## How to choose between data science and computer science - -Let's get into the details to help you decide which field fits you best. Choosing between both fields involves understanding your strengths, interests, and the job market. - -![How to choose between computer and data science](https://assets.roadmap.sh/guest/how-to-choose-between-data-science-and-computer-science-zo7p5.png) - -**Your strengths and educational background** -The path you choose boils down to what you're into, what you're good at, and your educational background. Computer science might be a good fit if you're into how computers work and creating software systems. An academic background in computer science or engineering also makes you a good fit. - -If you like finding hidden patterns in data and solving problems, then data science could be for you. You'll also be a good fit if you've studied mathematics, economics, or computer science. - -**Earning potential and industry demand** -Many people ask: **Which pays more, data science or computer science?** Well, both fields pay high salaries and are in high demand in the industry. - -Data scientists are in high demand across various sectors, like healthcare and finance. [According to Indeed](https://www.indeed.com/career/data-scientist/salaries?from=top_sb), the average salary for a data scientist in the United States (USA) across all industries is **$123,141.** - -![Average salary for a data scientist](https://assets.roadmap.sh/guest/average-salary-for-a-data-scientist-vnv54.png) - -Computer science professionals like software engineers and software developers are also in demand. [According to Indeed](https://www.indeed.com/career/computer-scientist/salaries?from=top_sb), computer scientists in the USA make around $121,452 a year on average. - -![Average salary for a computer scientist](https://assets.roadmap.sh/guest/average-salary-for-a-computer-scientist-88wog.png) - -How much money you earn can depend on where you live, your field, and your skills. roadmap.sh provides [computer](https://roadmap.sh/computer-science) and [data science](https://roadmap.sh/ai-data-scientist) resources to help you improve in both fields. - -## FAQ: Data science or computer science? - -The following are answers to common questions to help you start your career as a computer and data scientist. - -![Frequently asked questions about computer and data science](https://assets.roadmap.sh/guest/faq-data-science-or-computer-science-he3pu.png) - -**Is data science harder than computer science?** -Data science and computer science are challenging fields in their different ways. So, it is difficult to say one field is harder than another. The difficulty level varies based on personal viewpoints, interests, and capabilities. - -**Is data science still in demand in 2025?** -Yes, data science is still in demand in 2025. [The US Bureau of Labor Statistics](https://www.bls.gov/ooh/math/data-scientists.htm#:~:text=in%20May%202023.-,Job%20Outlook,on%20average%2C%20over%20the%20decade.) predicts a 36% increase in data science jobs from 2023 to 2033. - -**How long does it take to complete most computer science programs?** -Computer science programs like a bachelor's degree often take four years to complete. Master's programs take one to three years, depending on your pace or the school. Bootcamps and online certifications, however, may take less time. - -**Is data science more focused on mathematics or computer science?** -Both fields are important parts of data science—you can't have one without the other! It uses statistics and mathematical concepts to analyze data and computer science for handling data and building models. The balance may vary depending on the specific role, project, or focus within data science. - -## What Next? - -Deciding between data science and computer science does not need to be a difficult task. Figure out what works best for you by thinking about what you like, what you're good at, and what you want to achieve. - -Also, you don't have to limit yourself to just one field. Many people use data and computer science skills to solve problems daily. So, it is very normal to be good in both fields. - -However, if you're a beginner, focus on improving at one before learning another. [roadmap.sh](http://roadmap.sh) provides roadmap guides for you to learn [computer](https://roadmap.sh/computer-science) and [data science](https://roadmap.sh/ai-data-scientist). Both roadmaps contain resources and everything you need to get started. diff --git a/src/data/guides/ai-data-scientist-vs-cyber-security.md b/src/data/guides/ai-data-scientist-vs-cyber-security.md deleted file mode 100644 index 4ee6e2a04..000000000 --- a/src/data/guides/ai-data-scientist-vs-cyber-security.md +++ /dev/null @@ -1,181 +0,0 @@ ---- -title: "Data Science vs. Cyber Security: Which Is Best for You?" -description: "Cyber security fights threats. Data science uncovers insights. If you're deciding between them, here's a practical guide based on skills, job roles, and career growth." -authorId: ekene -excludedBySlug: '/ai-data-scientist/vs-cyber-security' -seo: - title: "Data Science vs. Cyber Security: Which Is Best for You?" - description: "Cyber security fights threats. Data science uncovers insights. If you're deciding between them, here's a practical guide based on skills, job roles, and career growth." - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-vs-cybersecurity-xjcfh.jpg' -isNew: true -type: 'textual' -date: 2025-04-14 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Data Science and Cyber Security compared](https://assets.roadmap.sh/guest/data-science-vs-cybersecurity-xjcfh.jpg) - -Data science and cyber security are two of the most in-demand career options in IT. Both career paths deal with analyzing complex data and come with various challenges. For beginners, a structured learning path in [data science](https://roadmap.sh/ai-data-scientist) or [cybersecurity](https://roadmap.sh/cyber-security) can be a great starting point. However, if you already have good knowledge of both fields but find it difficult to choose the right one for your career, this guide will definitely help. - -If you want to analyze large volumes of data and uncover deep insights, data science is a great fit for you. On the other hand, if you are passionate about protecting digital assets, preventing cyber threats, and ensuring information security, cyber security might be the better path for you. - -Data science vs. cyber security is a common debate among tech professionals when choosing a career path, as both fields offer high salaries, strong career growth, and essential roles in modern businesses. This results in confusion among career switchers and younger graduates struggling with the choice of cyber security or data science. So, let's compare the two based on these factors to make it easier for you to choose a career path. - -In this guide, you'll learn the job responsibilities of cyber security and data science, the required skills, and career prospects. - -## Differences between data science and cyber security - -The following table provides a quick comparison between cyber security and data science. - -| | **Data Science** | **Cyber Security** | -| ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **Key role** | Data Scientists use computer science and statistical analysis and organizational data to analyze and solve complex business problems. | Cyber security professionals use security tools, networking fundamentals, and incorporate risk management strategies to prevent security breaches and threats. | -| **Industries** | Healthcare, technology, finance. | Software and SaaS companies, healthcare, government, etc. | -| **Tools** | TensorFlow, Pandas, Jupyter Notebooks. | Firewalls, SIEM systems, intrusion detection tools, and encryption software. | -| **Skills** | Expertise in programming, machine learning skills, statistics, data visualization, big data, natural language processing, data manipulation and data wrangling. | Knowledge of security protocols, networking fundamentals, risk management skills, incident response, penetration testing and ethical hacking. | -| **Career paths** | Data scientists, machine learning engineer, AI analysts, data engineer, data analyst. | Security analyst, cyber security engineer, information security manager, network engineer, security auditor. | -| **Salary range** | For the data science field, the salary ranges from $53,925 to $161,715 per year. | For cyber security professionals, the salary ranges from $91,123 to $136,104 per year. | - -## What are data science and cyber security? - -Data science is a multidisciplinary field focused on extracting knowledge and insights from data to solve business problems. It involves statistical methods, programming, machine learning, data analytics. and domain expertise to build models and analyze structured and unstructured data while ensuring data integrity. This field is widely applied across industries such as healthcare, tech, and finance. - -Cyber security, on the other hand, focuses on protecting data and systems from cyber threats and unauthorized access. As a cybersecurity professional, you will use firewalls, intrusion detection systems, and encryption to secure systems and data while maintaining data integrity. Cyber security is crucial in software companies, SaaS platforms, healthcare, and government sectors. - -Despite their differences, both data science and cyber security rely on data. For example, if you work as a data scientist in an eCommerce company, you may analyze consumer related data to recommend products and drive sales. If you are a cyber security expert in the same company, your job will be to detect and prevent fraud by analyzing transaction patterns for suspicious activity. While data science focuses on using secure data to generate insights, cybersecurity relies on large datasets to identify threats and vulnerabilities. - -As data grows in volume and importance, the demand for data scientists and cyber security professionals will continue to rise. If you're considering a career in either field, you'll find plenty of opportunities. - -Now, let's look at the skills you need to get started in each field. - -## Data science vs. cyber security: Skills and tools required for each field - -The data science role generally involves building predictive models based on the analyzed datasets, data cleaning, and visualizations. Cyber security, on the other hand, involves tasks like monitoring systems and performing risk assessments to protect digital assets. - -![Data science vs. cyber security: Skills](https://assets.roadmap.sh/guest/data-science-vs-cybersecurity-skills-e6x9b.png) - -Let's go into detail and explain the different skill sets and tools that aspiring data scientists and cyber security experts must have. - -### Skills and tools for data science - -The process of extracting data, which encompasses both structured and unstructured information, requires fundamental skills and tools that can handle the initial analysis and management challenges effectively. If you're preparing for a data science job, you must have the technical skills to perform analyses and the interpersonal skills to communicate your findings effectively to stakeholders and team members. - -Some of the key skills an interviewer looks for in a data scientist profile include: - -- **Expertise in programming languages:** A certified data scientist must be good at different programming languages, including [Python](https://roadmap.sh/python), R, SAS, and [SQL](https://roadmap.sh/sql). You must know the basics of these languages to sort, analyze, and manage large datasets easily. -- **Machine learning skills:** You should be able to sort and synthesize data while making better predictions based on your collected data. Additionally, you should be able to implement appropriate algorithms to solve complex problems. -- **Statistics:** The data scientist's role involves to build models based on analysis and draw actionable insights. You must know how to use statistical analysis concepts like probability distribution and linear regression to write high-quality models and algorithms for your project. -- **Data visualization:** Apart from analyzing data, you must also learn skills on how to represent your work to stakeholders. So, to create charts and graphs, you must know how to use data visualization tools like Tableau and Power BI. -- **Data wrangling:** Expertise in techniques like cleaning and organizing complex data and pattern recognition to ease the analysis process. - -If you're applying for a data science role, you should have expertise in tools such as TensorFlow, Pandas, Jupyter Notebooks, and Tableau. - -### Skills and tools for cybersecurity - -As a cyber security expert, you must have the technical expertise and skills to identify and respond to threats in real time. - -You must excel in the following cyber security skills and tools to qualify for different career opportunities within the cyber security field: - -- **Networking:** You must deeply understand network protocols, operating systems, security concepts, and architectures to assess vulnerabilities before attackers exploit systems. -- **Risk management:** Expertise in managing and mitigating cyber security risks is essential. These skills allow you to better assess the likelihood and impact of incoming known threats. -- **Ethical hacking:** As an ethical hacker, you'll use the same tools and techniques as malicious hackers but with permission from the system owner. By knowing how they act and their strategies, you can improve your network security and overall security posture. -- **Knowledge of security protocols:** A strong understanding of the various security protocols, like encryption protocols, network security protocols, authentication protocols, etc., is a must. - -In addition, you must know how to use firewalls, security information and event management (SIEM) systems, intrusion detection tools, and encryption software to qualify for different career options in cybersecurity. Managing data security is not a simple task; it requires a sharp mind and analytical thinking to beat cyber threats and respond to security breaches. - -Let's move forward to learn about the salary range for each job position to make it easier for you to compare and select based on your requirements. - -## Career opportunities and salary insights - -As more businesses use data to make decisions, the demand for data science professionals is high among different industries. Cyber security experts are also in constant demand with the introduction of new and advanced technologies like artificial intelligence (AI) and cloud computing. - -![Data science vs cyber security: Career opportunities](https://assets.roadmap.sh/guest/data-science-vs-cybersecurity-career-opportunities-adyg5.png) - -Let's investigate the salary insights and career options available in both fields to identify your area of interest. - -### Data science career paths and salary insights - -Planning to proceed with the data science field? If yes, the following career options are available for consideration: - -- Data scientist -- Machine learning engineer -- AI specialist - -**Data scientist:** In this role, you'll find patterns in complex datasets, create algorithms and data models, deploy data tools, perform data analysis, and predict solutions to stakeholders based on the analysis. You can incorporate machine learning and deep learning practices to improve data quality and predict outcomes. As a data scientist, you must have a good command of your communication skills apart from technical skills and statistical modeling expertise. - -The average salary of an employee pursuing a career as a senior data scientist in the United States is $123,069 per year. - -**Machine learning engineer:** If you apply for this position, your task will be to research and build machine learning algorithms and artificial intelligence systems. Machine learning professionals are also responsible for optimizing frameworks and performing statistical analysis. - -The average salary for this job role in the United States ranges around $161,715 annually. - -**AI specialist:** Your role as an AI specialist is to develop and optimize AI systems. Hence, ensure you know the basics of AI concepts and programming. In addition, you might be responsible for AI system integration, designing AI strategies, and reinforcement learning. - -According to ZipRecruiter, the average salary for an AI Specialist in the United States is $53,925 a year. - -Data engineer, data analyst, and data architect are a few more job roles within data science that you can apply for. - -### Cyber security career paths and salary insights - -If you have an interest in cybersecurity, you can choose from the following career path options: - -- Security analyst -- Cyber security engineer -- Information security manager - -**Security analyst:** As a Cyber security analyst, your main role is to identify and troubleshoot problems within security systems and protect sensitive data. Security analyst professionals monitor systems and perform compliance control testing to minimize security risks. Based on their analysis, you can also recommend reducing risk and securing systems. - -According to Indeed, the average salary of a cyber security analyst in the United States is $91,123 per year. - -**Cyber security engineers:** Cyber security engineers are the front warriors who design and configure solutions against cyber criminals. You will have to act as an ethical hacker to strategize and identify vulnerabilities before attackers exploit them. - -ZipRecruiter states that the average salary of an employee pursuing a career in this position in the United States is $122,890 per year. - -**Information security manager:** In this role, your main focus will be on developing and implementing security policies. You will also handle incident response plans, threat monitoring, and the implementation of security best practices for protecting computer systems. - -According to ZipRecruiter, the average salary of a Certified Information Security Manager in the United States is $136,104 per year. - -Let's dive in and take a closer look at the work environment in both fields to help you decide which one is best for you. - -## Work environment differences - -The work environments in data science and cyber security differ significantly, appealing to distinct personalities and work styles. - -Data science typically offers a more collaborative and research-oriented atmosphere. Imagine teams of data analysts, data science professionals, and researchers brainstorming, analyzing historical data, sharing insights, and working together to find solutions for complex datasets. - -Flexibility is often a key feature, both in terms of working hours and location. While deadlines exist in data science, the project-based nature often allows for a more measured pace, with timelines that can stretch over weeks or months. This allows for deep dives into data, thorough data analysis, and the development of sophisticated models. - -You will be more attracted to the data science field if you enjoy intellectual exploration and the satisfaction of uncovering hidden patterns. Data science is great if you like intellectual exploration, problem solving and finding hidden patterns. It's creative and encourages deep analysis of complex problems, so if you like structured but exploratory work, it's a good fit. - -Cyber security, on the other hand, is a high-stakes, fast-paced world. The focus is on real-time threat prevention, monitoring security infrastructure, and rapid response to security incidents. - -Security professionals need to be on high alert, anticipating and countering attacks on systems. It's intense work, with split second decisions and big consequences. - -The nature of cyber security often demands irregular hours, especially during security crises. A major breach can require teams to work around the clock to contain the damage and restore systems. While this can be incredibly demanding, it can also be immensely rewarding for those who thrive in high-pressure situations and enjoy the thrill of the chase. - -Whether you are planning for cyber security or data science, consider all aspects, including the work environment. - -## Cyber security and data science: Which path is best for you? - -It's best to keep your interests, skills, and personality in mind when selecting between cyber security and data science. - -![Data science vs. cybersecurity: Which is better?](https://assets.roadmap.sh/guest/data-science-vs-cybersecurity-9bhbk.png) - -If you find yourself drawn to the world of data, enjoy the challenge of extracting meaning from raw data, and prefer coding and statistical analysis over other practices, data science might be the right path for you. Do you get excited about building predictive models, uncovering trends, and using data to solve real-world problems? If so, you'll likely find the intellectual challenges of data science stimulating. - -On the other hand, if your passion lies in protecting systems, staying one step ahead of hackers, and responding to high-stakes challenges, cybersecurity could be a more fulfilling career choice. If you love understanding cybercriminals' psychology and preventing them from achieving their goals, this one could be your career goal. - -If you still have questions about cyber security and data science, try taking online courses or doing small projects in each field to find your real interest. You can also work with datasets, experiment with different algorithms, connect with a data engineer, or explore basic cyber security concepts. This hands-on experience will give you valuable insights into your preferences. - -## What's Next? - -Cyber security or data science, whichever path you choose, make sure it fulfills your needs and interests. If working on numbers and models is your thing, data science is the right path for you. However, cyber security could be a better option if you are more inclined toward protecting systems and taking on challenges. - -Choosing between cyber security and data science can be a difficult decision, but this guide has provided key insights to help you evaluate your interests. To further explore your options, check out [cyber security](https://roadmap.sh/cyber-security) and [data science](https://roadmap.sh/ai-data-scientist) roadmaps to gain deeper insights into each field. You can also join online courses and get relevant certifications or connect with our experts on the [Discord community](https://roadmap.sh/discord) to stay up-to-date! - diff --git a/src/data/guides/ai-data-scientist-vs-data-analytics.md b/src/data/guides/ai-data-scientist-vs-data-analytics.md deleted file mode 100644 index c4d2f5d0c..000000000 --- a/src/data/guides/ai-data-scientist-vs-data-analytics.md +++ /dev/null @@ -1,174 +0,0 @@ ---- -title: 'Data Science vs. Data Analytics: Which is Right for You?' -description: 'Data science vs. Data analytics? This guide breaks down roles, tools, and growth opportunities for aspiring data professionals.' -authorId: william -excludedBySlug: '/ai-data-scientist/vs-data-analytics' -seo: - title: 'Data Science vs. Data Analytics: Which is Right for You?' - description: 'Data science vs. Data analytics? This guide breaks down roles, tools, and growth opportunities for aspiring data professionals.' - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-vs-data-analytics-3ol7o.jpg' -isNew: false -type: 'textual' -date: 2025-02-06 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Data science vs data analytics comparison](https://assets.roadmap.sh/guest/data-science-vs-data-analytics-3ol7o.jpg) - -If you enjoy spotting patterns, analyzing trends, and driving business strategies, a career in [data analytics](https://roadmap.sh/data-analyst) might be your ideal fit. On the other hand, if algorithms, coding, and diving into uncharted territory excite you, a career in [data science](https://roadmap.sh/ai-data-scientist) could be the better path. - -As someone whose work spans both fields and involves managing data to solve business challenges, I've seen how both data science and analytics shape business success. - -Businesses rely heavily on insights, whether streamlining operations, predicting future trends, or crafting innovative strategies. Both data analytics and data science are pivotal to this process, but they approach problems differently. - -As a data analyst, you'll focus on making sense of data through trends, visualizations, and actionable insights. As a data scientist, you'll work on building predictive data models and solving complex problems using advanced machine learning techniques. - -But the big question is: Which path aligns with your goals? - -The answer lies in your interests, strengths, and career aspirations. In this guide, I'll take you through the key differences between data science and data analytics and show you how they complement each other. You'll learn which skills are needed in each role and what career paths and opportunities they offer. By the end, you'll clearly know which role fits you best and how to start building your future. - -The table below summarizes the key differences between data science and data analytics. - -| | **Data Science** | **Data Analytics** | -|---------------------|----------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------| -| **Key Role** | Uses statistical analysis and computational methods to gain insights from complex, structured and unstructured data. | Analyzes data collected from different sources, generates insights, and makes smart data-driven decisions. | -| **Skills** | Machine learning, reinforcement learning techniques, data wrangling, big data technologies, cloud computing, and predictive analytics. | Proficient in data collection, SQL, knowledge of advanced Excel functions, data visualization, critical thinking, and create visual presentations. | -| **Tools** | TensorFlow, PyTorch, Jupyter Notebooks, and GitHub/Git. | SQL, Excel, Tableau, Power BI, OpenRefine or Google Analytics | -| **Career Paths** | Data Scientist > Machine Learning Engineer > AI Specialist | Data Analyst > Business Intelligence Manager > Chief Data Officer (CDO) | -| **Salary Range** | For data scientist job positions, salary ranges from $119,040 to $158,747 per year. | For data analysis job positions, salary ranges from $82,000 to $86,200 per year. | - -## What are data science and data analytics? - -Data science and data analytics are two multidisciplinary fields that share the goal of helping organizations make smarter decisions, but they achieve this in different ways. - -Data science uses advanced tools like machine learning and AI to extract insights from large, complex data sets. As a data scientist, your role is to uncover patterns and build predictive models that solve problems like fraud detection, ad optimization, and trend forecasting. Tools like [Python](https://roadmap.sh/python), Apache Spark, and [SQL](https://roadmap.sh/sql) are key to this work. - -Data analytics, meanwhile, focuses on interpreting existing data to uncover trends and deliver actionable insights. As a data analyst, you'll use data analytics tools like Excel, Tableau, and Power BI to identify patterns, forecast sales, analyze customer behavior, and guide strategy. This work is grounded in understanding what has already happened to influence future business decisions. - -By understanding the distinct purposes of these roles, we can examine how they interact to drive meaningful results. - -## How do data science and data analytics complement each other? - -For example, consider an ecommerce business whose sales have declined over the past quarter. A data analyst would start by examining historical sales data using tools like Excel or SQL to identify patterns and uncover potential causes, such as price changes or shifting customer demographics. These findings would then inform the data science team. - -The data scientists would take this further by building predictive models to analyze future sales trends. They might incorporate additional features, like customer feedback or competitor pricing, to provide proactive recommendations that could reverse the decline, such as adjusting pricing strategies or launching targeted campaigns. - -Therefore, data analytics helps you answer the "**what**,** why**, and **where**" questions. For example, you can use it to ask, "what caused past sales?" or "why did customer churn go up in Q1?" or "where is our main revenue coming from?" By looking at historical data, data analytics gives you the answers you need to improve and build better strategies. - -The data science process takes it a step further by answering the "**why**" and **"how**" questions, like why sales went down and how to fix it. Data science leverages machine learning algorithms and predictive techniques to provide you with the right solutions to move forward. - -Next, explore the specific job roles and responsibilities in data science and data analytics. - -## **Data science vs. data analytics:** **Job role and responsibilities** - -Here are the primary responsibilities that define the role of a data analyst and how they contribute to enabling data-informed business decisions. - -![Data science & data analytics: Roles and responsibilities](https://assets.roadmap.sh/guest/data-analysts-vs-data-science-role-and-responsibilities-0p0wv.png) - -**Key responsibilities of data scientist** -As a data scientist, you'll work on complex tasks, such as building models, designing algorithms, and experimenting with data to uncover unknown outcomes. For example, to predict which customers are likely to cancel their subscriptions, you will analyze past customer behavior using predictive models to identify patterns. - -Here is a quick overview of your key responsibilities as a data scientist: - -- **Data collection and management:** Collect data from many sources, often dealing with structured and unstructured data. Your focus will be on getting the data for analysis, which can be simple to very complex, depending on the problem. -- **Build predictive models:** Apply machine learning techniques to predict future behaviors, such as customer churn or sales demand. -- **Design algorithms:** Develop new algorithms to optimize business operations, such as fraud detection systems or creating personalized recommendations for customers. -- **Data experimentation:** Identify hidden patterns and extract meaningful insights from large and unstructured data sets. - -**Key responsibilities of data analyst** -As a data analyst, you focus on understanding structured data to answer specific business queries and make smart decisions. For example, to identify sales trends over the past year, you will perform the following tasks: - -- **Data collection and processing:** Gather information from different sources and remove inaccuracies or unnecessary data. Use data-cleaning method to maintain accuracy and prepare data for analysis. -- **Data analysis:** Interpret formatted and cleaned data using statistical tools and advanced modeling techniques. -- **Data reporting:** Create clear and concise reports to share with business. -- **Business recommendations:** Provide recommendations based on what you found to improve sales, efficiency, and performance. - -Let's dig into the tools and skills needed for your selected job role. - -## **Data science vs. data analytics: Skills and** **tools** - -When you choose between becoming a data analyst or a data scientist, understanding the essential skills and tools for each role is crucial. Both positions demand analytical proficiency, but their technical requirements and focus areas differ significantly. - -![Data science & data analytics: Skills](https://assets.roadmap.sh/guest/data-analytics-vs-data-science-skills-ftf50.png) - -Let's explore the key skills and tools to help you make the right decision. - -**Data scientist skills and tools** -As a data scientist, you'll have technical, analytical, and problem-solving skills to handle large and complex datasets. Some of the main skills and tools that interviewers look for are: - -- **Programming skills:** Mastery of Python and R is essential for data science tasks, including statistical analysis and machine learning model development. -- **Machine learning expertise:** Knowledge of supervision and reinforcement learning techniques. Additionally, you should have an understanding of algorithms and clustering methods. -- **Big data tools:** Hadoop and Apache Spark are a must for distributed storage and big data analysis. -- **Mathematics and statistics**: Advanced knowledge of mathematics and statistics is essential for building models and deriving insights. - -You should also focus on mastering tools like [TensorFlow](https://roadmap.sh/cpp/libraries/tensorflow), PyTorch, Jupyter Notebooks, [GitHub/Git](https://roadmap.sh/git-github), SQL, Apache Spark, Hadoop, [Docker](https://roadmap.sh/docker), [Kubernetes](https://roadmap.sh/kubernetes), and Tableau. Data visualization, Scikit-learn, and version control systems are also important for data science. - -**Data analyst skills and tools** -As a data analyst, your role focuses on data interpretation for business decisions. Some of the main skills and tool proficiencies you'll need to excel in this role are: - -- **SQL (Structured Query Language):** Knowledge of SQL is necessary for querying, managing and retrieving data from databases. -- **Advanced Excel skills:** Strong Excel skills, including pivot tables, VLOOKUP, and data analysis functions, are necessary to organize and analyze data. -- **Data visualization:** Ability to create good-looking charts and dashboards using tools like Tableau and Power BI is a must for presenting insights in a clear and effective way. -- **Critical thinking:** Strong analytical and critical thinking skills to identify trends and derive meaning from data. - -Check out the [Data Scientist](https://roadmap.sh/ai-data-scientist) and [Data Analyst](https://roadmap.sh/data-analyst) roadmaps for a structured approach. These will help you decide what to learn and where to focus. By following them, you can prioritize what to learn, focus on high-demand areas, and not feel overwhelmed. Also, join local or online meetups to connect with professionals and participate in hackathons to get hands-on experience and add to your portfolio. - -Let's move forward to understand different career trajectories that fall under data science and data analysis. Also, check out the salary ranges for each job profile. - -## **Data science vs. data analytics: Career paths and salary insights** - -If you're looking into data science or data analytics careers, you're entering a field with huge growth. Both have their own focus, but there's a lot of overlap in skills, tools, and methodologies, so it's easier to move between roles or expand your skills across both domains. - -![Data science vs. data analytics: Career paths and salary insights](https://assets.roadmap.sh/guest/data-scentists-vs-data-analysts-career-paths-and-salary-insights-oclgw.png) - -Here is a quick overview of role transitions, salary ranges, and the steps you can take to advance your career as a data scientist and data analyst. - -**Data science career paths and salary insights** -As a data scientist engineer, these are the roles that are typically available to you throughout your career: - -- Data scientist -- Machine learning engineer -- AI specialist - -**Data scientist:** As a data scientist, you will analyze large datasets, develop predictive data models, and implement algorithms to extract insights. Additionally, you must have knowledge of machine learning basics, structured data, statistical modeling, and communication skills. - -In 2024, the [average salary](https://www.datacamp.com/blog/data-science-salaries) for a data scientist is $123,069 per year in the United States. - -**Machine learning engineer:** In this role, you'll focus on developing and deploying machine learning models in production environments. This role requires technical expertise in software engineering, computer science, big data technologies, and scalable systems. You must have knowledge of advanced machine learning, computer science, cloud computing, and software development lifecycle (SDLC) knowledge. - -According to [Indeed](https://www.indeed.com/career/machine-learning-engineer/salaries), the average salary for a machine learning engineer in the United States is $161,715 per year. - -**AI specialist:** You'll focus on designing cutting-edge AI solutions, managing teams of data professionals, and driving strategic artificial intelligence initiatives for organizations. You even perform research on emerging spot trends and implement AI frameworks. - -According to [Glassdoor,](https://www.glassdoor.co.in/Salaries/us-ai-specialist-salary-SRCH_IL.0,2_IN1_KO3,16.htm) the estimated salary of an AI specialist job profile in the US is $129,337 per year, with an average salary of $105,981 per year. - -**Data analytics career paths and salary insights** -If you're leaning towards data analytics, here are some common job titles for you, along with salary details for each role: - -- Data analyst -- Business intelligence manager -- Chief data officer - -**Data analyst:** This role involves collecting, cleaning, and analyzing data to generate actionable insights. You'll work on dashboards, reporting, and descriptive analytics using tools like Excel and Tableau. Additionally, you must have basic programming knowledge, data visualization, and data mining skills. - -In 2024, the estimated [average salary](https://www.indeed.com/career/data-analyst/salaries) of a data analyst ranges around $80,811 per year depending on experience, location, and specific skills in demand. - -**Business intelligence manager:** As a business intelligence manager, you'll lead data reporting and visualization strategies, manage data accuracy, and design scalable solutions. Communication skills and proficiency in business intelligence tools are key to this role. - -[ZipRecruiter](https://www.ziprecruiter.com/Salaries/Business-Intelligence-Manager-Salary) reported that the salary of a business intelligence manager in the US ranges from $29,500 to $158,500. - -**Chief data officer (CDO):** Responsible for an organization's data strategy, data governance, and data leveraging for competitive advantage. For this job role, you must have the necessary skills, such as data governance, data strategy, data engineering, and management of complex architecture. - -In 2024, the estimated total pay for a chief [data officer](https://www.glassdoor.co.in/Salaries/us-chief-data-officer-salary-SRCH_IL.0,2_IN1_KO3,21.htm) is $373,952 per year in the US. - -## **What** **Next** - -Once you've decided to pursue a career in data science or data analytics, the next step is figuring out where to start. Our [AI-Data Scientist](https://roadmap.sh/ai-data-scientist) and [Data Analyst roadmap](https://roadmap.sh/data-analyst) are designed to help you with that by breaking down the skills, tools and concepts into smaller steps. Whether you are drawn to the complexity of algorithms or analyzing trends, both paths offer rewarding opportunities for personal and professional growth, these roadmaps will give you a clear structure to build a solid foundation and move forward with confidence. - -Remember, the key to success in both fields is a commitment to continuous learning. For detailed overview of any specific role, join the [Discord community](https://roadmap.sh/discord) and stay informed! diff --git a/src/data/guides/ai-data-scientist-vs-data-engineering.md b/src/data/guides/ai-data-scientist-vs-data-engineering.md deleted file mode 100644 index 5ac540e0a..000000000 --- a/src/data/guides/ai-data-scientist-vs-data-engineering.md +++ /dev/null @@ -1,193 +0,0 @@ ---- -title: 'Data Science vs. Data Engineering: Lessons from My Journey' -description: 'I’ve worked on both data science and data engineering projects - here’s what I’ve learned and how you can choose the best path for your career.' -authorId: ekene -excludedBySlug: '/ai-data-scientist/vs-data-engineering' -seo: - title: 'Data Science vs. Data Engineering: Lessons from My Journey' - description: 'I’ve worked on both data science and data engineering projects - here’s what I’ve learned and how you can choose the best path for your career.' - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-vs-data-engineering-bychp.jpg' -isNew: true -type: 'textual' -date: 2025-03-24 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Data science vs data engineering explained](https://assets.roadmap.sh/guest/data-science-vs-data-engineering-bychp.jpg) - -Many aspiring professionals wonder about the difference between data science and data engineering. Although both fields involve working with data, they focus on different aspects of the data lifecycle. - -When I started my tech career, I was torn between data science and data engineering. I spent weeks researching and experimenting with projects to find where my strengths fit best. Both fields are closely related but yet distinct. - -If you love working with data, solving complex problems, and using data to guide decisions, you should consider [data science](https://roadmap.sh/ai-data-scientist), but if you enjoy building and maintaining data infrastructure and systems, I suggest you consider data engineering. - -In this guide, I will share the key lessons I learned, the challenges I faced, and how you can decide which career is right for you. - -The table below summarizes the differences between data science and data engineering. - -## Differences between data science and data engineering - -| | Data science | Data engineering | -| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | -| Focus | Analyzing and interpreting data using statistical models, machine learning, and analytics to obtain value and make predictions. | Designing, building, and maintaining data pipelines and infrastructure for data storage and accessibility. | -| Skills | Machine learning, statistics, data visualization, and predictive models. | Knowledge of data infrastructure, database management, and cloud tools. | -| Tools | Python, R, SQL, Tableau, PyTorch, Power BI, Tensorflow. | Python, Scala, R, Java, Apache Spark, Kafka, Snowflake, Databricks. | -| Educational background | Computer science, statistics, and mathematics. | Computer science, computer engineering, and software engineering. | -| Career paths | Data scientist, [Data analyst](https://roadmap.sh/data-analyst), Machine learning engineer. | Data engineer, Big data engineer, Data architect. | - -## What is data science and data engineering? - -When I worked on my first data-driven project, I realized how much I enjoyed working with raw data. But I also saw how much effort went into setting up pipelines and structuring data before I could analyze it. That’s when I understood the fundamental difference: Data engineers build the highways, while data scientists drive on them to find insights. - -Data science is a field that combines mathematics, statistics, analytics, artificial intelligence, and machine learning to analyze large amounts of data to detect hidden patterns, generate actionable insights, and predict trends. As a data scientist, you’ll analyze and interpret complex data to help organizations and businesses make informed decisions. - -On the other hand, data engineering involves designing and building systems for aggregating, storing, and analyzing data. As a data engineer, you’ll build data warehouses to empower data-driven decisions. You will focus on developing pipelines to collect, clean, and transform data, ensuring it is accessible for analysis by the data science team. - -## Key responsibilities of a data scientist - -Your responsibilities as a data scientist will vary depending on the industry, company size, and project focus. To provide more context, I will explain the responsibilities of a data scientist using a project I worked on during my career. The project involved customer churn analysis for a client in the hospitality industry. - - -![Data Scientist Responsibilities](https://assets.roadmap.sh/guest/data-scientist-skills-and-tools-wi5tz.png) - - -1. **Business understanding:** As a data scientist, you’ll work closely with stakeholders to understand the context and gain business insights. - In the customer churn analysis project, I worked closely with the client to understand the business context and gain insights. This provided an in-depth understanding of the hospitality sector and the different terms used. Without this, solving the business problem would have been almost impossible. - -2. **Data ingestion:** Your work as a data scientist involves gathering raw data from different sources. It is your responsibility as a data scientist to collect all the relevant data to solve the current business problem. - - When I worked on the customer churn analysis project, I had to pull data from multiple sources (customer transaction logs, website analytics, and CRM tools). But before I could even begin the analysis, the data engineers had to build ETL pipelines to collect and clean this data. - -3. **Data processing and data storage:** This is one of your core responsibilities as a data scientist. It involves cleaning and transforming the ingested data into suitable formats for analysis and saving data storage systems. - - In the customer churn analysis project, the data ingested were unusable and had to be cleaned so they could be used for analysis. I used tools like Python and NumPy to process the data and saved the processed data in a database. - -4. **Data analysis:** As a data scientist, you’ll analyze processed data using statistical analysis and data visualization tools like Matplotlib, Seaborn, and Pandas. You’ll explore the data to understand relationships, hidden patterns, characteristics, and anomalies. You will use charts and graphs to make the data more understandable and easier to comprehend. - - In the customer churning analysis project, I analyzed the data by identifying the number of returning customers, the customers who came only once, the day of the week with the most customers, and so on. With this analysis, I could identify a trend in the data, which helped in predictive model building. - -5. **Model building:** As a data scientist, you’ll build predictive models and machine learning algorithms to forecast future trends. The machine learning algorithms you build can be categorized into two types: - - Supervised learning: Examples include linear regression, decision trees, and k-nearest neighbors. - - Unsupervised learning: Examples include clustering and dimensionality reduction. - - 6. **Reporting:** As a data scientist, it is your responsibility to interpret and communicate the results of data analysis and predictions. It is not enough to analyze and explore the data; you have to communicate them clearly so that the key stakeholders easily understand them, influence decision-making, and achieve business goals. - -## Skills and tools needed to succeed as a data scientist - -To succeed as a data scientist, you need to have a balance of technical and analytical skills. Some of these skills and tools I recommend include: - -![Data Scientist Skills and Tools](https://assets.roadmap.sh/guest/data-scientist-skills-and-tools-7fjnk.png) - - -1. **Programming languages:** As a data scientist, you should know some programming. The two most common programming languages data scientists use are [Python](https://roadmap.sh/python) and R. - -2. **Mathematics and statistics:** To succeed as a data scientist, you must know mathematics and statistics. They help you understand your data and know which statistical model to apply. Knowledge of mathematical concepts like calculus, probability theory, and linear algebra are fundamental to your success as a data scientist. - -3. **Data visualization:** This involves using graphs, charts, and maps to present data in an understandable and accessible format. It is important to know how to visualize data. Some of the tools used to create visualizations include Matplotlib, Seaborn, ggplot2, and Pandas. - -4. **Database management systems:** Database management systems are applications that interact with users, other applications, and the database to fetch and analyze data. As a data scientist, you will interact a lot with databases, and you should know how to write queries to communicate with them. - - [SQL](https://roadmap.sh/sql) is a programming language used to manage and communicate with relational databases. Examples of relational databases are MySQL, [PostgreSQL](https://roadmap.sh/postgresql-dba), and Microsoft Server SQL. There are also NoSQL databases that store unstructured data. Examples include [MongoDB](https://roadmap.sh/mongodb), Neo4j, and Cassandra. - -5. **Machine learning and artificial intelligence:** As a data scientist, you should have an understanding of machine learning. Machine learning can be divided into supervised, unsupervised, deep, and reinforced learning. Some key tools used by data scientists include Scikit-learn, TensorFlow, and PyTorch. - -## Key responsibilities of a data engineer - -To explain the key responsibilities of a data engineer, I will use an IoT project I worked on to provide more context. The project involved developing an IoT data pipeline to transmit data from a customer’s IoT devices in the field to a storage system. Your key responsibilities as a data engineer include: - -![Data Engineer Responsibilities](https://assets.roadmap.sh/guest/data-engineer-job-description-i0k7i.png) - -1. **Data pipeline development:** A data pipeline is a method of ingesting data from different data sources, transforming the data, and then transferring it to storage and processing systems. You are responsible for building and maintaining data pipelines as a data engineer. The different types of data pipelines include: - - Batch processing pipelines - - Data streaming pipelines - - Data integration pipelines - - Cloud-native data pipelines - In the IoT project, the data engineers designed the data pipeline used to transmit data from the IoT devices to the data storage system. The pipeline was a messaging system into which the IoT devices published data, which was ingested and stored in a database. - -2. **Data architecture design:** As a data engineer, you will build, design, and manage data lakes and warehouses for data storage and retrieval. You will work with cloud platforms ([AWS](https://roadmap.sh/aws), GCP, Azure) to develop scalable and reliable storage solutions. - -3. **Database management:** As a data engineer, you’ll manage and optimize storage solutions, which include relational databases, document databases, and data lakes. These data storage solutions store big data for analysis and prediction for data integrity, performance, and accessibility. - -4. **ETL processes development:** Extract, Transform, Load (ETL) is a process that cleans and organizes data from multiple data sources into a single and consistent data set for storage and further processing. As a data engineer, you are responsible for developing and maintaining the ETL process for proper data integration and onward passage of the data to be used by data scientists. - - The data from the IoT devices was in different formats, so the data engineers had to build an ETL process to transform the data for onward processing and storage. - -5. **Real-time data streaming:** Data engineers process real-time data from different data sources, which can be triggered via events or observers. As a data engineer, you design systems that respond to real-time data, and use tools like Google Publish and Subscribe and Azure Publish and Subscribe to transmit and process it. - - The data sent from the IoT devices was real-time data, and the data engineers built the messaging system that streamed the data. - -6. **Data governance and security:** Data engineers are responsible for compliance with data privacy laws. They implement data validation and integrity checks, which results in clean data and data reliability. They also make sure that sensitive user data is protected and not exposed without sufficient clearance. - -## Skills and tools needed to succeed as a data engineer - -To succeed as a data engineer, you need a combination of programming, data management, data pipelines, and cloud computing skills. I will explain the skills below. - - -1. **Programming languages:** You should know programming and scripting. Python is one of the most common programming languages you will use as a data engineer. Python is used for data processing, creating ETL pipelines, and automation. Other programming languages used are [Java](https://roadmap.sh/java), [SQL](https://roadmap.sh/sql), and Scala. Bash scripting is also a necessary skill you should have for workflow automation. - -2. **Databases:** As a data engineer, you are expected to know how to design and maintain database management systems. You will use relational databases and NoSQL databases to store and retrieve data. Examples of relational databases include PostgreSQL, MySQL, and Microsoft SQL Server. Examples of NoSQL databases include [MongoDB](https://roadmap.sh/mongodb), [Redis](https://roadmap.sh/redis), and DynamoDB. NoSQL databases are used to store unstructured data. - -3. **Data warehouses:** A data warehouse is a system that gathers data from different sources and stores them in a central location. It help prepare data for data analytics, machine learning, and artificial intelligence. - - As a data engineer, you should know how to work with data warehouses and also design and maintain them. There are cloud-based data warehouses and on-premise data warehouses. Cloud-based data warehouses are provided by cloud platforms and they include Amazon Redshift, Google BigQuery, and Azure Synapse Analytics. Examples of on-premise data warehouses include SAP BW/4HANA and IBM Db2 Warehouse. - -4. **Data lakes:** Data engineers use data lakes to store structured and unstructured data in their original formats. They store various types of data in different formats and provide a central repository for data analysis workloads. Examples of data lakes include AWS S3, Azure Data Lake, and Google Cloud Storage. - -5. **ETL processes:** Data engineers use ETL processes to automate the storage and retrieval of data in a database. The data is extracted from its source, transformed into the required format using automated scripts and programs, and then loaded to its destination. As a data engineer, you should know how to design ETL processes and build data pipelines. - -![Data Engineer Skills and Tools](https://assets.roadmap.sh/guest/data-engineer-skills-and-tools-rqgpa.png) - -## Data scientist vs. Data engineer: What career opportunities are available to you? - -Both professionals are in high demand in several industries, including health care, tech, finance, and retail. - -The typical career path in data science includes: - -- Junior data scientist -- Data scientist -- Senior data scientist -- Machine learning engineer - -The career path of a data engineer is similar to that of a data scientist. A typical data engineering career path includes: - -- Junior data engineer -- Data engineer -- Senior data engineer -- Data architect -- Senior data architect - -## Salaries and job market trends - -One thing that surprised me was how salaries vary depending on industry and specialization. In my experience, companies in finance and healthcare tend to pay data scientists more, while big tech firms offer strong compensation for data engineers working on large-scale infrastructure. - -According to Indeed, the average annual [data scientist salary](https://www.indeed.com/career/data-scientist/salaries) in the US is $125,156 with a range between $79,612 and $196,755. - -![Data Scientist Salary in the United States](https://assets.roadmap.sh/guest/data-scientist-salary-in-the-united-states-of-america-97xy4.png) - -The average [data engineer salary](https://www.indeed.com/career/data-engineer/salaries) in the US is $125,758, with a range between $82,340 and $192,069. - -![Data Engineer Salary in the United States](https://assets.roadmap.sh/guest/data-engineer-salary-in-the-united-states-of-america-1f12t.png) - -Both data scientist and data engineer roles are in high demand with the rise in AI. Companies are constantly hiring data scientists and data engineers. On Indeed, there are more than 10,000 openings for data scientists and more than 5,000 in the US alone. - -Data scientists and data engineers will also be very instrumental and in high demand in the future. According to the [US Bureau of Labour Statistics](https://www.bls.gov/ooh/math/data-scientists.htm), there will be 20,800 new openings for data scientists each year for the next decade. - -## Data science vs. Data engineering: Which path fits you better? - -Deciding whether to pursue data science or data engineering depends on your interests, strengths, and career goals. - -If you enjoy solving analytical problems, working with algorithms, and guiding business decision processes, then you should consider data science. Do you enjoy building large-scale systems and data infrastructure and ensuring data pipelines run smoothly? Then, you should consider data engineering. - -Do you understand statistics, mathematics, data analysis, and visualization well? Data science is well-suited for you. Data engineering is the right fit if you have strong programming skills and are knowledgeable in system design and architecture. - -## Next steps - -If I had to start over, I’d begin with small projects. My first real learning moment came when I built a basic recommendation system for movie ratings using [Python](https://roadmap.sh/python) and Pandas. If you're considering data engineering, setting up an ETL pipeline with Apache Airflow is a great starting point. Don't just read—build. - -You should also follow a structured learning path. roadmap.sh provides you a structured [data science](https://roadmap.sh/ai-data-scientist) roadmap where you can track your progress and share it on your profile. You could also customize your roadmap based on your learning needs. diff --git a/src/data/guides/ai-data-scientist-vs-machine-learning.md b/src/data/guides/ai-data-scientist-vs-machine-learning.md deleted file mode 100644 index 60c14f14a..000000000 --- a/src/data/guides/ai-data-scientist-vs-machine-learning.md +++ /dev/null @@ -1,181 +0,0 @@ ---- -title: 'Data Science vs Machine Learning: How are they different?' -description: 'Excited about a career in data science or machine learning? Learn the differences, key skills, tools, and how to choose the role that aligns with your ambitions' -authorId: ekene -excludedBySlug: '/ai-data-scientist/vs-machine-learning' -seo: - title: 'Data Science vs Machine Learning: How are they different?' - description: 'Excited about a career in data science or machine learning? Learn the differences, key skills, tools, and how to choose the role that aligns with your ambitions.' - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-vs-machine-learning-gaa7s.jpg' -isNew: false -type: 'textual' -date: 2025-02-06 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Data science vs machine learning comparison](https://assets.roadmap.sh/guest/data-science-vs-machine-learning-gaa7s.jpg) - -If you're excited by the idea of extracting insights from data to guide decisions, a career in [data science](https://roadmap.sh/ai-data-scientist) might be for you. On the other hand, if you're drawn to creating the algorithms behind AI systems and building intelligent applications, machine learning could be your ideal path. - -Both fields are at the forefront of innovation, driving transformative technologies like ChatGPT, DALL-E, and Gemini. These advancements, used across industries like healthcare, finance, and tech, owe their success to the growing collaboration between data science and machine learning. As AI becomes more accessible with tools from companies like OpenAI and AWS, the demand for experts in these fields is only increasing. - -So, how do you choose between these high-demand, rewarding careers? As an ML Engineer with experience on projects that span across data science and machine learning, I have gained a deep understanding of the overlaps and differences between these fields. In this guide, I will explain the responsibilities of each role, highlight their key differences, and outline the essential skills for success. By the end, you'll be better equipped to pick the path that matches your interests, strengths, and career goals. - -The table below summarizes the key differences between data science and machine learning you should consider to help you evaluate which one best fits your career goals: - -| **Aspect** | **Data science** | **Machine learning** | -| ------------------------------------- | ----------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | -| **Ideal for you** **i\*\***f\***\*…** | You enjoy exploring and analyzing data to extract insights and inform decisions. | You are passionate about creating algorithms and systems that learn and improve automatically. | -| **Educational background** | Strong foundation in statistics, data analysis, and visualization tools. | Strong foundation in mathematics, programming, and AI techniques. | -| **Career opportunities** | Data scientist, [Data Analyst](https://roadmap.sh/data-analyst), Business Intelligence Analyst. | Machine learning Engineer, AI Researcher, Deep Learning Specialist. | -| **Industries** | Healthcare, finance, marketing, e-commerce, and government sectors. | Technology, autonomous systems, robotics, fintech, and R&D labs. | -| **Skills you'll need** | Data cleaning, exploratory analysis, storytelling, and domain expertise. | Proficiency in machine learning libraries, algorithm design, and optimization. | -| **Growth potential** | Strong demand across industries as companies seek to become more data-driven. | High demand in tech-driven fields as AI adoption accelerates. | -| **Creativity vs. technical** | Balances creativity in visualizations with technical skills in data processing. | Heavily focused on technical and mathematical skills for problem-solving and predictive analytics. | -| **Long-term vision** | Perfect if you want to lead data-driven strategy or explore business analytics. | Ideal if you aim to innovate in AI, robotics, or advanced tech solutions. | - -Before looking at these features in detail, let's take a closer look at these two fields. - -## What is data science? - -Data science is a field that combines techniques from statistics, domain knowledge, computer science, and data analysis to gain insights from structured and unstructured data. As a data scientist, you'll use tools, machine learning models, and algorithms to understand data and drive decision-making. You'll use these insights to help businesses increase profits, create innovative products and services, improve systems, and solve problems across various industries. - -**Key Components of Data Science** -The key components of data science involve the following processes: - -![Data science Components](https://assets.roadmap.sh/guest/data-science-process-flow-57cx5.png) - -1. **Data collection**: The first step is to gather raw data from various sources such as APIs, sensors, databases, and web scraping. -2. **Data cleaning and preparation**: After collecting the raw data, it must be cleaned by removing inaccuracies, handling missing values, and formatting it for analysis. -3. **Exploratory Data Analysis (EDA)**: Use statistical methods and visualization techniques to explore the data, identify trends, patterns, and anomalies, and gain a better understanding of the dataset. -4. **Data modeling and machine learning**: Apply machine learning algorithms to build models that can identify patterns, predict outcomes, and automate processes. -5. **Data visualization**: Use tools like charts, graphs, and dashboards to present insights and communicate findings clearly to stakeholders. -6. **Deployment and monitoring**: Implement data models in real-world applications and continuously monitor their performance to ensure they remain accurate and effective. - -### Essential Skills and Tools You Need for a Successful Data Science Career - -To build a successful career in data science, knowledge of a programming language like [Python](https://roadmap.sh/python) is essential. Beyond that, as a data scientist, you'll need to develop expertise in the following skills and tools: - -- **Programming languages**: You'll need proficiency in [SQL](https://roadmap.sh/sql), R, SAS, and others to collect, manipulate, and manage both structured and unstructured data. -- **Mathematics, statistics, and probability**: A strong grasp of these concepts will enable you to build accurate models and make data-driven decisions with confidence. -- **Data wrangling and visualization**: You'll be responsible for cleaning, transforming, and visualizing data using tools like Matplotlib, Seaborn, Power BI, and Tableau to present insights effectively. -- **Machine learning and predictive modeling**: Knowledge of machine learning algorithms and techniques for building models that can make predictions and automate decision-making processes. -- **Data analysis tools**: Familiarity with tools like Jupyter Notebooks, Pandas, NumPy, Apache Spark, and Scikit-learn will help you analyze and process data with ease. -- **Cloud platforms**: Experience with cloud platforms such as [AWS](https://roadmap.sh/aws), Azure, and Google Cloud will enable you to leverage cloud resources for scalable computing and efficient model deployment. - -## What is machine learning? - -Machine learning is a branch of Artificial Intelligence (AI) that enables computers to learn from data and make predictions without being explicitly programmed. Rather than manually coding rules for every scenario, machine learning models learn from available data to perform tasks such as fraud detection, recommendation systems, natural language processing, and image recognition. It can be broadly categorized into the following types: - -![Illustration representing machine learning](https://assets.roadmap.sh/guest/machine-learning-classification-szzk0.png) - -- **Supervised learning**: In supervised learning, you train a model labeled data to predict outcomes and identify patterns. For example, you can use a dataset with details like location, square footage, and other factors to teach the model how to predict house prices. -- **Unsupervised learning**: In unsupervised learning, the model is trained on unlabeled data and discovers patterns or groupings on its own. For example, you can use it to segment customers based on their purchasing habits without specifying predefined categories. -- **Reinforcement learning**: In reinforcement learning, you train a model through a trial-and-error process to achieve the best outcomes. For instance, self-driving cars use reinforcement learning by continuously learning to recognize obstacles, road signs, and blockages through repeated experiences. - -### Key Components of Machine Learning - -The key components of machine learning involve the following processes: - -![Machine Learning Pipeline](https://assets.roadmap.sh/guest/machine-learning-pipeline-v5daz.png) - -1. **Data processing**: The foundation of any machine learning system is data. For the model to work effectively, it requires selecting and preparing the right data for training. -2. **Feature engineering**: This step involves selecting the right variables (features) that the model can use to make accurate predictions. For example, a housing price prediction model would need features like the number of bedrooms, square footage, and location to make reliable predictions. -3. **Model selection**: Choosing the appropriate model for a specific task is crucial. For instance, a **Linear Regression** model is suitable for predicting continuous values, while **Neural Networks** are better suited for tasks like image recognition. -4. **Model training**: In this process, the selected model is fed with training data to help it learn patterns. The model's internal parameters are adjusted to minimize prediction errors. -5. **Validation and testing**: After training, the model's performance is evaluated using a separate validation dataset. A final test dataset is used to ensure the model performs well in real-world scenarios. -6. **Deployment**: Once the model is ready, it is integrated into real-world applications or deployed on cloud platforms to be accessed by third party users. -7. **Model monitoring and maintenance**: After deployment, the model must be regularly monitored and updated to maintain accuracy and adapt to changes over time. - -### Essential Skills and Tools You Need for a Successful Machine Learning Career - -To build a successful career in machine learning, you'll need to develop the following essential skills and become familiar with key tools: - -- **Strong understanding of mathematics and statistics**: You'll need a solid understanding of linear algebra, calculus, probability, and statistics to grasp how machine learning algorithms function. -- **Proficiency in programming languages**: Python and R are must-haves for implementing machine learning models and handling data efficiently. -- **Data handling and preprocessing skills**: You need a solid understanding of how to clean, preprocess, and transform raw data into a format suitable for training models. -- **Knowledge of machine learning algorithms**: Understanding algorithms like Linear Regression, Q-learning, and K-means and knowing when to apply them will help you tackle diverse challenges. -- **Model evaluation and tuning**: You need to master techniques to evaluate model performance (e.g., accuracy, precision, recall) and fine-tune hyperparameters to improve results. -- **Familiarity with libraries and frameworks**: Hands-on experience with Scikit-learn, TensorFlow, Keras, and other popular libraries and frameworks will help you build and deploy machine learning models efficiently. -- **Cloud Platforms for Infrastructure**: Familiarity with cloud platforms like AWS, Google Cloud, and Azure will let you manage the infrastructure needed for large-scale machine learning projects. - -While data science and machine learning share common skills, tools, and workflows, they differ significantly in their approaches, methodologies, and focus areas. The table below summarizes the key differences between machine learning and data science: - -| **Category** | **Data science** | **Machine learning** | -| ------------------------------ | ---------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ | -| **Mathematics and statistics** | Strong knowledge of statistics, probability, and linear algebra. | Deep understanding of calculus, linear algebra, and optimization techniques. | -| **Programming languages** | Python, R, and SQL for data manipulation, statistical analysis, and visualization. | Python, [C++](https://roadmap.sh/cpp), and [Java](https://roadmap.sh/java) for implementing machine learning algorithms. | -| **Data handling** | Data wrangling, data cleaning, and data visualization. | Data preprocessing, feature engineering, and handling large datasets. | -| **Machine learning basics** | Basic understanding of supervised, unsupervised learning, and regression. | Advanced knowledge of machine learning algorithms, including deep learning. | -| **Business acumen** | Ability to translate business problems into data solutions. | Focus on technical problem-solving without a primary business context. | -| **Tools and frameworks** | Tableau, Excel, Hadoop, Pandas, Matplotlib, and Scikit-learn. | TensorFlow, PyTorch, Scikit-learn, Keras, and XGBoost. | -| **Data visualization** | Building dashboards, reports, and storytelling through data. | Creating visualizations primarily for model performance evaluation. | -| **Communication skills** | Strong emphasis on presenting insights to non-technical stakeholders. | Less focus on communication; more technical documentation. | -| **Problem-solving approach** | Emphasis on interpreting data to guide decisions. | Emphasis on building models to automate decision-making. | -| **Domain knowledge** | Domain expertise in industries like healthcare, finance, marketing, etc. | Less reliance on specific domain knowledge; more generalizable algorithms. | -| **Software engineering** | Less focus on software engineering practices. | Strong focus on scalable system design and code optimization. | -| **Algorithm understanding** | Basic knowledge of algorithms for data analysis. | Deep understanding of algorithms, including neural networks and gradient descent. | - -Now that we've covered data science and machine learning regarding processes, tools, similarities, and differences, let's explore the key points you should consider to make the right career decision. - -## Data science vs. machine learning: Which career path fits your background? - -If you have a background in statistics, data analysis, and business intelligence, data science could be the perfect match for you. Data science is all about turning raw data into valuable insights that businesses can act on. Your familiarity with working with data makes it easier to spot patterns and trends, which is a key part of what data scientists do. It's a role that requires both creative problem-solving and analytical thinking. - -Machine learning, on the other hand, is better suited for you if you have a strong foundation in mathematics, programming, and computer science. A machine learning expert builds algorithms that can learn from data without explicitly programming them. It requires a solid grasp of concepts like linear algebra, calculus, probability, and algorithm design. If these are skills you already have, this path is a natural fit for you. - -## Data science vs. machine learning: Which path pays off in the long run? - -Both machine learning and data science offer exciting and rewarding career paths, especially with the growing demand for AI across industries. - -In data science, you typically start your career with entry-level positions like Data Analyst or Business Intelligence Analyst and can evolve into leadership roles like Chief Data Officer. This career path is appealing because it allows you to collaborate with both technical teams and business stakeholders, solving real-world problems with data-driven insights. - -A typical career progression in data science involves: - -- Data Analyst -- Data Scientist -- Senior Data Scientist -- Analytics Manager -- Chief Data Officer - -![Average data scientist salary in the US](https://assets.roadmap.sh/guest/average-data-scientist-salary-in-the-us-i48ea.png) - -Machine learning careers tend to offer slightly higher salaries due to the specialized skills in programming and algorithm design. A machine learning professional typically starts as a machine learning engineer and can progress to advanced roles like Head of AI/ML, focusing on developing intelligent systems and cutting-edge AI solutions. - -A typical career progression in machine learning involves: - -- Machine Learning Engineer -- AI Specialist -- Deep Learning Engineer -- AI Research Scientist -- Head of AI/ML - -![Average machine learning Engineer salary in the US](https://assets.roadmap.sh/guest/average-machine-learning-engineer-salary-in-the-us-zdz8c.png) - -Both paths provide excellent growth opportunities, but your choice should align with your background and long-term career goals. - -## Data science vs. machine learning: Which opportunities are available for you? - -In terms of opportunities, data scientist are in higher demand across various industries that need data-driven insight to make decisions and improve their processes. These include companies within healthcare, finance, marketing, retail, and e-commerce. So, if your goal is to work in diverse industries that rely on data insights, then data science is an ideal choice. - -Machine learning, on the other hand, is more likely to work in technology-focused sectors where prediction, automation, and intelligence systems are at the core of their operation. These include industries like robotics, research and development labs, and autonomous vehicles. As a result, opportunities in machine learning may be harder to come by compared to data science, as machine learning professionals often work in more specialized and focused environments. However, if you're drawn to tech-driven fields involving autonomous systems or AI research, machine learning could be an ideal path for you. - -## Machine learning vs. data science: Which balances creativity and tech better? - -Data science strikes a balance between technical expertise and creativity. It's all about taking complex data and transforming it into meaningful insights that non-technical stakeholders can easily understand. If you enjoy solving problems creatively and telling compelling stories with data, data science would be a great fit for you. - -Machine learning, on the other hand, is more technical and involves developing algorithms. It involves designing, training, and optimizing models that enable machines to learn and make predictions. If you love tackling technical challenges like developing algorithms and building AI models from the ground up, then a career in machine learning would be ideal for you. - -As a rule of thumb, use the decision tree table to choose between data science and machine learning: - -![Data Science vs Machine Learning Decision Tree](https://assets.roadmap.sh/guest/data-analytics-vs-data-science-skills-ftf50.png) - -Choosing between data science and machine learning ultimately depends on your interests and career goals. Both fields offer rewarding opportunities and career growth. The key is to understand what excites you most: extracting meaning from data or building intelligent systems. - -If you're considering starting a career as a data scientist, explore our comprehensive [data science roadmap](https://roadmap.sh/ai-data-scientist) for actionable steps and valuable resources to get started. diff --git a/src/data/guides/ai-data-scientist-vs-software-engineering.md b/src/data/guides/ai-data-scientist-vs-software-engineering.md deleted file mode 100644 index e50fe1fab..000000000 --- a/src/data/guides/ai-data-scientist-vs-software-engineering.md +++ /dev/null @@ -1,179 +0,0 @@ ---- -title: "Data Science vs Software Engineering: Which to Choose?" -description: "Both software engineering and data science offer exciting opportunities, but they require different skill sets. This breakdown helps you find your ideal fit." -authorId: william -excludedBySlug: '/ai-data-scientist/vs-software-engineering' -seo: - title: "Data Science vs Software Engineering: Which to Choose?" - description: "Both software engineering and data science offer exciting opportunities, but they require different skill sets. This breakdown helps you find your ideal fit." - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-vs-software-engineering-t7okw.jpg' -isNew: true -type: 'textual' -date: 2025-04-14 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Data Science and Software Engineering compared](https://assets.roadmap.sh/guest/data-science-vs-software-engineering-t7okw.jpg) - -Software engineering and [data science](https://roadmap.sh/ai-data-scientist) aren't completely separate; they're more like two overlapping fields. They share similarities, but each has its own focus. Software engineering centers on creating functional products and systems, while data science focuses on extracting insights from data and building predictive models. - -You might want to pick software over data science if you're good at or interested in building applications, solving engineering problems, and working on system architecture. Otherwise, you might want to go for data science if you're good at or interested in analyzing data, finding patterns, and developing models to make data-driven decisions. - -So, how do you choose between these two exciting and in-demand careers? When I started my career, I constantly switched between writing software that powered applications and analyzing data to drive decisions. Both fields fascinated me, but they required different skill sets and ways of thinking. If you're torn between data science and software engineering, this guide will help you understand their key differences and choose the path that aligns with your strengths and interests. - -## Data science vs software engineering - -The table below highlights the major differences between software engineering and data science to help you evaluate which one might be the right fit for you: - -| **Aspect** | **Software Engineering** | **Data Science** | -| -------------------------- | ------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------- | -| **Focus** | Software engineers focus on building, maintaining, and scaling software applications | Data scientists focus on analyzing data to extract insights and build predictive models | -| **Key skills** | Programming, system design, DevOps, cloud computing | Statistics, machine learning, data visualization | -| **Languages** | Python, Java, Go, JavaScript, C# | Python, R, SQL, Scala | -| **Tools and frameworks** | Git, Docker, Kubernetes, React, Spring Boot | TensorFlow, PyTorch, Pandas, Jupyter, Spark | -| **Primary goal** | Deliver reliable and scalable software systems | Extract meaningful insights from data | -| **Work environment** | Agile development, collaboration with product teams | Research-oriented, collaboration with analysts and business teams | -| **Mathematical intensity** | Moderate (algorithms, data structures) | High (statistics, probability, linear algebra) | -| **Problem-solving type** | Debugging, optimizing performance, scalability | Pattern recognition, prediction, optimization | -| **Typical output** | Websites, mobile apps, cloud platforms, APIs | Reports, machine learning models, dashboards | -| **Career paths** | Backend Developer, Frontend Developer, DevOps Engineer | Data Analyst, Data Scientist, ML Engineer | -| **Best for people who** | Enjoy building and improving systems that users interact with | Love working with data, uncovering patterns, and making predictions | - -Before looking at these aspects in detail, let's take a closer look at these two fields. - -## What is software engineering? - -Software engineering is the process of designing, building, testing, and maintaining computer programs. It involves using structured methodologies, programming knowledge, and best practices to develop software that is reliable, efficient, and easy to maintain. - -For example, when you work on an app like WhatsApp as a software engineer, you work on different parts, like handling how messages are sent, how the app looks and feels, and much more. - -## Key components of software engineering - -The key aspects of software engineering involve the following: - -![Key components of software engineering](https://assets.roadmap.sh/guest/key-components-of-software-engineering-pll8t.png) - -1. **Software development lifecycle (SDLC)**: Every application goes through several stages before it's ready for use: - 1. **Planning and requirements**: Define what the software should do and identify the necessary tools. - 2. **Design**: Outline how the software will function and how different parts will interact. - 3. **Coding**: Write the actual program using languages like [Java](https://roadmap.sh/java), [Python](https://roadmap.sh/python), or [JavaScript](https://roadmap.sh/javascript). - 4. **Testing**: Find and fix bugs before launching. - 5. **Deployment**: Release the software for users. - 6. **Maintenance**: Update and improve the software by adding features or fixing issues. -2. **Software design and architecture**: This involves structuring the software and deciding how different components work together. It's like designing a building. You need a solid foundation and a layout that's organized and easy to navigate. -3. **Programming and Development**: Writing code is a major part of software engineering. You'll use languages like Python, JavaScript, or Java to develop web, mobile, and backend applications. -4. **Testing and Quality Assurance**: Before releasing software, testing helps identify and fix bugs so users don't run into problems. -5. **Deployment and DevOps**: Once the software is ready, it needs to be deployed properly. This includes setting up **CI/CD pipelines** for automated testing and deployment, as well as monitoring systems to track performance and apply updates when needed. - -## Essential skills you need for a successful software engineering career - -To build a successful career in software engineering, you'll need a mix of technical and soft skills like: - -- **Programming languages**: Coding is the heart of software engineering. A strong grasp of languages like JavaScript, Python, and Java will help you build both frontend and backend systems. -- **System design**: This involves architecting software so that they are scalable, maintainable, and efficient. You'll make key decisions about databases, API structures, and how different services communicate within an application. -- **Database management**: Data is an essential part of your application. You'll need to know how to structure it, optimize queries, and keep it secure. For example, in an **online banking system**, customer balances must be stored securely while still allowing fast retrieval for transactions. -- **Cloud platforms**: Cloud computing enables businesses to run applications without managing physical servers. Familiarity with platforms like [AWS](https://roadmap.sh/aws), Google Cloud, or Azure will help you deploy and scale applications efficiently. -- **Communication and teamwork**: You'll often need to explain technical concepts to non-technical stakeholders, write clear documentation, and collaborate with teams across different functions. Strong communication skills will help you work effectively and drive projects forward. - -## What is data science? - -Data science is the process of collecting, analyzing, and interpreting large amounts of data to find useful insights. It combines programming, statistics, and problem-solving to help businesses and organizations make better decisions. - -For example, when Netflix suggests movies based on what you've watched before, it's using data science to predict what you might like. - -## Key components of data science - -The key aspects of data science involve the following: - -![Key components of data science](https://assets.roadmap.sh/guest/key-components-of-data-science-ahbrz.png) - -1. **Data collection**: This is the first step in a data science project. As a data scientist, you'll gather information from sources like websites, databases, and sensors for further analysis. For example, in an e-commerce project, you'll collect data on products people buy to understand shopping trends. -2. **Data cleansing and preparation**: The data you collect from various sources are raw and won't come in ready for analysis. You need to clean and organize the raw data to remove missing entries, duplicated items, and missing headers. -3. **Data analysis and visualization**: This involves the use of charts and graphs to find patterns and trends. For example, you can analyze social media company data to see how many users are active at different times of the day. -4. **Machine learning and predictions**: Beyond manually identifying trends and patterns, you'll also use machine learning algorithms to train computers to recognize patterns and make predictions. For instance, a data scientist working in a bank can use machine learning to detect fraud by spotting unusual transactions. - -## Essential skills you need for a successful data science career - -To build a successful career in data science, you'll need a mix of technical and soft skills like: - -- **Programming languages**: A solid understanding of Python or R is essential for data analysis, machine learning, and automation. SQL is also crucial for retrieving and managing data from databases. -- **Statistics and mathematics**: You need a strong grasp of probability, distributions, and calculus, as they come in handy when interpreting data and building accurate models. -- **Data visualization and machine learning**: You need a solid understanding of how to transform complex numbers into easy-to-understand visuals like bar charts, line graphs, and heat maps. Beyond visualization, you also need a good understanding of how to build models that enable computers to recognize patterns and make predictions without being explicitly programmed. -- **Big data tools**: You need a good knowledge of big data technologies like Hadoop and Spark that let businesses process and analyze large datasets quickly. For example, you can use Spark to analyze millions of users data on a social media platform to personalize content recommendations. -- **Problem solving and critical thinking**: As a data scientist, you'll need to question data sources, uncover hidden insights, identify biases, and draw accurate conclusions from complex datasets. -- **Communication and teamwork**: Even the best insights are useless if they aren't clearly communicated. You'll need to translate technical findings into actionable insights that business teams can understand and act on. - -Now that we've covered software engineering and data science regarding processes, tools, and required skills, let's explore why software engineering might be a better option for you or if data science is better suited. - -## What your day-to-day involves - -If you're excited by how big tech companies like Uber or Netflix design, develop, and maintain mobile apps, backend systems, or large distributed systems, then software engineering might be a great fit for you. As a software engineer, you will focus on building functional and efficient digital products, using and developing the technical skills that interest you. - -If you're more interested in extracting insights from data rather than building software systems, data science might be a better fit. As a data scientist, your core focus will be analyzing large datasets, applying machine learning models, and deriving actionable business insights. Unlike software engineering, this role involves more statistical modeling and data interpretation than designing or developing applications. - -## What skills and background do you currently have? - -Are you familiar with a programming language? Have you taken any courses related to computer science or how the Internet works? - -If your answer to some of these questions is **"YES,"** and you enjoyed learning these skills, software engineering might be a great fit. Your existing knowledge and background will be valuable as you build your career in software development. - -However, data science might be worth considering if you're more comfortable working with numbers, statistics, or data visualization rather than building software systems. A strong foundation in statistics, data analysis, and business intelligence is key in this field, making it a good fit for you as you enjoy working with data rather than writing application logic. - -## Do you enjoy working in a collaborative environment? - -As a software engineer, you will work in teams that include both technical (software developers, product managers, and designers) and non-technical (sales and marketing) stakeholders. A typical workday involves writing code, fixing bugs, reviewing pull requests, and discussing features. If you enjoy working collaboratively and like having teammates with whom to brainstorm ideas, then software engineering could be a great fit for you. - -While data scientists also collaborate with other stakeholders, their teams are usually smaller, especially in startups or smaller companies. In many cases, a single person or a very lean team often handles data-related tasks, meaning the role may involve more independent research, analysis, and reporting rather than large-scale team collaboration. If you prefer deep, focused work with fewer distractions, data science might be the better choice. - -## Data science vs software engineering: Where do you see yourself in the future? - -Both software engineering and data science are rewarding career paths with diverse opportunities for growth and specialization. - -In software engineering, you can work across various industries that continuously adapt and innovate with technology. You also have the flexibility to explore related career paths like: - -- Frontend developer -- Backend developer -- Fullstack developer -- DevOps engineer -- Senior software engineer -- Cloud engineer -- Software architect - -This broad range of roles increases your chances of landing a job, as there are many positions to apply for. - -The average salary of a software engineer in the US is about [$105,596 per annum](https://www.indeed.com/career/software-engineer/salaries), and software engineers tend to earn even more with experience and specialized skills. - -![Average software engineer salary in the US](https://assets.roadmap.sh/guest/average-software-engineer-salary-in-the-us-n2eha.png) - -In data science, the career path is more specialized, with key roles like: - -- Data analyst -- Machine learning engineer -- Business intelligence analyst -- AI researcher - -While the average salary for a data scientist may be slightly higher than that of a software engineer, software engineering offers greater career flexibility and a wider range of job opportunities. If you're unsure which path to take, consider whether you enjoy building systems (software engineering) or analyzing data to extract valuable insights (data science). - -The average salary of a data scientist in the US is about [$123,111 per annum](https://www.indeed.com/career/data-scientist/salaries?from=top_sb). - -![Average data scientist salary in the US](https://assets.roadmap.sh/guest/average-data-scientist-salary-in-the-us-1-qcl4f.png) - -While data science is leading in AI adoption, software engineering is just as important. Data scientists focus on tasks like collecting, cleaning, and preparing data and training machine learning (ML) models that power AI systems. - -But AI isn't just about data science; software engineers play a key role too. They build the infrastructure and pipelines needed to deploy and manage these models, making it possible to run them efficiently on large datasets. - -Plus, AI APIs have made it much easier for software engineers to integrate AI into applications without deep expertise in machine learning. So, if you're worried about missing out on the AI wave, don't be. You can still do amazing work in the space. - -Ultimately, both software engineering and data science are rewarding careers. To determine which one suits you best, consider doing a litmus test: - -- Try building a small application using version control and deploying it to see if software engineering interests you. -- Analyze a dataset with Python, create visualizations, and experiment with basic machine learning to see if data science excites you. - -If you're leaning toward software engineering, check out our software development roadmaps for [frontend](https://roadmap.sh/frontend), [backend](https://roadmap.sh/backend), or [full stack](https://roadmap.sh/full-stack) development. If data science resonates with you, explore our [data science roadmap](https://roadmap.sh/ai-data-scientist) to get started. - diff --git a/src/data/guides/ai-data-scientist-vs-statistics.md b/src/data/guides/ai-data-scientist-vs-statistics.md deleted file mode 100644 index febd8374b..000000000 --- a/src/data/guides/ai-data-scientist-vs-statistics.md +++ /dev/null @@ -1,197 +0,0 @@ ---- -title: "Data Science vs Statistics: How do they compare?" -description: "Not sure whether to pursue data science or statistics? This guide breaks down the key differences, career paths, and skills you need to make an informed choice." -authorId: ekene -excludedBySlug: '/ai-data-scientist/vs-statistics' -seo: - title: "Data Science vs Statistics: How do they compare?" - description: "Not sure whether to pursue data science or statistics? This guide breaks down the key differences, career paths, and skills you need to make an informed choice." - ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-vs-statistics-e3rtw.jpg' -isNew: false -type: 'textual' -date: 2025-03-24 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Data science vs statistics compared](https://assets.roadmap.sh/guest/data-science-vs-statistics-e3rtw.jpg) - -Data science and statistics are promising career paths that offer great opportunities to work with data to solve problems and guide decision-making in various industries. But how do you determine which one is a good career for you? If you're new to these fields, learning about their core concepts and career opportunities can help you get started. If you are a student or professional exploring data-driven careers, this guide will help clear up confusion and determine the path that aligns with your goals. - -When choosing between the two, think about the types of problems you like to solve. If you're interested in understanding why and how an issue occurred, uncovering insights from raw data, and building models to predict future outcomes, data science may be the right fit for you. On the other hand, if you prefer analyzing and interpreting quantitative data, identifying patterns, and making statistical predictions, statistics could be the better path. - -At first, data science and statistics might seem interchangeable. But as we dig deeper, you'll see how they differ. Statistics provides the foundation by defining methods, probabilities, and models. Data science builds on that foundation to extract insights from complex data and solve real-world problems. - -In this guide, I'll explain where [data science](https://roadmap.sh/ai-data-scientist) and statistics connect and differ. You'll learn more about the skills and tools necessary for each role and gain more clarity into the career prospects and opportunities they offer. By the time you finish, you'll have a clear vision of your ideal role and the confidence to start shaping your future. - -The following table provides a comparison of data science and statistics. - -| | Data Science | Statistics | -| --------------- | --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- | -| Key role | Uses statistics, computer science, and domain expertise to interpret complex datasets. | Uses statistical methods to analyze and make predictions for small, structured datasets. | -| Data processing | Handles large and complex datasets (big data). | Works with smaller datasets, emphasizes sampling techniques. | -| Tools | Python, R, SQL, Hadoop, and TensorFlow. | Statistical software like SAS, R, SPSS, and mathematical models. | -| Skills | Machine learning knowledge, programming skills, data mining, data visualization, business intelligence, and predictive analytics. | Proficient in probability, mathematical theory, statistical modeling, data analysis, and survey design. | -| Career paths | Data scientist, machine learning engineer, data analysts. | Statistician, biostatistician, research analyst. | -| Salary range | For data science career options, salary ranges from $81,273 to $161,715 per year. | For statistics career trajectories, salary ranges from $75,789 to $101,789 per year. | - -Data science vs. statistics: What sets them apart? - -Data science and statistics are two different areas of analytics, each with its role and methodology. Both use statistical methods, but the key difference lies in their application and purpose. - - -![Data science vs. statistics](https://assets.roadmap.sh/guest/data-science-vs-statistics-uxnkp.png) - - -Data science is a blend of statistics, computer science, and domain expertise. It focuses on analyzing large, complex datasets to uncover trends, predict outcomes, and drive business decisions. As a data scientist, you'll use machine learning, programming, and big data tools to solve real-world problems. - -Statistics, on the other hand, is rooted in mathematical theory and inference. As a statistician, you'll design experiments, develop models, and test hypotheses to draw conclusions from structured datasets. You'll work in industries like finance, healthcare, government, and research. - -The key difference is application. Statistics is theoretical and inference-driven, while data science applies statistical techniques in a computational, business-focused way. - -To get a clearer picture of both roles, let's look at the day-to-day tasks involved in each field. - -## Job roles and responsibilities - -Both data science and statistics rely on statistical methods and tools, but your day-to-day activities in each field will differ significantly. As a statistician, you focus on collecting, analyzing, and interpreting data to uncover patterns. As a data scientist, you go beyond that by leveraging machine learning, big data technologies, and programming to extract actionable insights. - -Now, let's take a closer look at their specific roles and responsibilities through a shared case study. - -### Key responsibilities of data scientists - -As a data scientist, you'll extract actionable insights from raw data to solve complex business problems. For example, imagine a pharmaceutical company is developing a new drug. As a data scientist, you might be analyzing clinical trial data to predict treatment efficacy for different patient groups. By applying machine learning models, you can find hidden patterns in patient responses, segment populations, and optimize treatment recommendations. - -The following are some of your day-to-day responsibilities as a data scientist: - -* Discovery: Understanding patient demographics, health conditions, and genetic factors to segment target groups. -* Data collection: Gathering patient records, clinical trial results, and external medical research data for analysis. -* Generate insights: Finding trends in treatment responses using advanced analytics and predictive modeling. -* Model large data sets: Using machine learning to forecast drug effectiveness across different conditions. -* Design algorithms: Building AI-driven models to personalize medication plans and optimize trial outcomes. - -### Key responsibilities of a statistician - -As a statistician, your focus is on designing experiments, analyzing data and applying statistical methods to evaluate results. - -Using the same pharmaceutical company case, as a statistician, you might be responsible for ensuring the clinical trial design is statistically sound and the results are statistically valid before regulatory approval. You would be conducting hypothesis testing, confidence interval analysis, and regression models to confirm that the drug is effective and safe. - -Your day-to-day responsibilities might include: - -* Designing surveys and experiments: Design a clinical trial or questionnaires for the new drug to verify it is statistically sound -* Testing hypotheses: Choosing the right statistical test based on the data type. -* Analyzing structured datasets: Summarizing data, analyzing, and modeling relationships between variables to predict outcomes. -* Data reporting: Using charts, graphs, and other visual aids to report complex information effectively. -* Business recommendations: Provide valuable insight and production strategies based on trial results. - -Let's dig into a few tools and skills you must be proficient in for your selected job role. - -## Essential skills and tools - -Whether you are planning to choose data science or statistics as your career, it is important to know the right tools and essential skills for future challenges. - -![Data science vs statistics: Skills](https://assets.roadmap.sh/guest/data-science-vs-statistics-skills-tbnm7.png) - -Both data science and statistics require a strong foundation in mathematics and analytical thinking skills. Professionals in these fields often pursue advanced degrees, such as a Master's or Ph.D., to enhance their expertise. Let's examine a few key skills and tools essential for each job profile. - -### Skills and tools of a data scientist - -Handling complex datasets, including both unstructured and structured data, requires a data scientist to possess strong technical and problem-solving skills. You should have the following skills for a data scientist job profile: - -* **Programming Skills:** To perform data science tasks smoothly, you must be proficient in [Python](https://roadmap.sh/python), [SQL](https://roadmap.sh/sql), [Java](https://roadmap.sh/java), and R programming languages. Additionally, you must be good at statistical analysis and mathematics. -* **Machine learning knowledge:** You should be able to choose and implement appropriate algorithms, such as linear regression, logistic regression, decision trees, random forests, and neural networks, based on the specific problem and dataset. -* **Data mining:** Knowledge of techniques like data cleaning, preprocessing, feature engineering, and pattern recognition are essential to extracting meaningful insights from big data. -* **Data visualization:** Tools like Tableau and Power BI can help create compelling charts and graphs for your big data. You must know how to use these tools effectively to communicate data insights. -* **Business intelligence:** Understanding business contexts and translating data insights into actionable business decisions is key. You must have strong communication skills and domain knowledge. - -Hadoop, Docker, TensorFlow, GitHub/Git, SQL, and Tableau are important for a data science job profile. Interviewers often prioritize candidates with educational qualifications in computer science, programming, and practical applications of data analysis techniques. - -### Skills and tools of a statistician - -A statistician's main responsibility is to analyze data to predict future events or trends. To achieve this goal, you must excel in the following skills and tools: - -* Mathematical theory: A strong understanding of mathematical concepts like linear algebra, calculus, etc., is essential to measure quantitative data. -* Probability: Proficiency in probability theory is essential for understanding and modeling uncertainty in data. -* Statistical modeling: Experience with various statistical models, such as time series analysis and linear regression, is also valuable. -* Survey design: Knowledge of designing questionnaires or sampling techniques is necessary to collect information from a group of people. - -R, SAS, SPSS, and Excel are a few popular tools you should master to perform statistical analysis and generate graphics. Also, candidates with qualifications in advanced statistical modeling, mathematical and statistical theory, and research methodologies have a better chance of getting selected. - -Let's move forward to different career options available under data science and statistics. Learning about their salary ranges will provide valuable insight into earning potential, helping you make an informed career decision. By comparing salaries, you can assess financial stability, ensuring that your chosen path aligns with your long-term financial goals. - -## Career prospects for both fields - -Data science is experiencing explosive growth across numerous industries, whereas the demand for statisticians remains strong and consistent. Let's explore the various job roles available in both fields and their salary ranges. - -### Data science career paths and salary insights -You can choose from the following careers if you are planning to proceed with data science: - -* Data scientist -* Machine learning engineer -* Data analyst - -**Data scientist:** A career in data science offers opportunities in various industries, including finance, healthcare, and technology. As a data scientist, you'll tackle unique problems, using statistical modeling and machine learning to drive business decisions and outcomes. You need to have a deep understanding of data manipulation and how data relates to the real world. - -Many data scientists use machine learning algorithms to build predictive models, identify trends, and make data-driven decisions. Strong communication skills and expertise in statistical modeling are essential for success in this field. You may start as a junior data scientist and improve your knowledge to lead as Head of Data Science over time. - -In the United States, the [average salary](https://www.datacamp.com/blog/data-science-salaries) of a data scientist is $123,069 per year. - -**Machine learning engineer:** A career as a machine learning engineer involves designing, building, and optimizing machine learning models used in various applications, from recommendation systems to fraud detection. Professionals in this field work on improving machine learning frameworks, monitoring model performance, and solving complex computational problems. To succeed, you need expertise in advanced machine learning, cloud computing, and the software development lifecycle (SDLC). - -Machine learning engineers are in high demand across finance, healthcare, e-commerce, and technology industries. You can start by working on implementing basic machine learning models; as you improve your skills and gain more knowledge, you can work towards a role overseeing ML infrastructure, defining best practices, and leading technical teams. - -In the United States, the [average salary](https://www.indeed.com/career/machine-learning-engineer/salaries) for a machine learning engineer is $161,715 per year. - -**Data analyst:** [Data analysts](https://roadmap.sh/ai-data-scientist/vs-data-analytics) play a crucial role in helping businesses make informed decisions by collecting, filtering, and analyzing data to answer specific business questions. You can use data visualization tools to present insights clearly to stakeholders. - -A career as a data analyst offers opportunities across various industries, including marketing, finance, and healthcare, making it a versatile and in-demand profession. The data analyst job profile includes various positions and levels, from basic reporting and performing deeper data analysis to making data-driven decisions for an organization. - -According to [Indeed](https://www.indeed.com/career/data-analyst/salaries), the average salary for a data analyst job profile in the United States is $81,273 annually. - -### Statistics career paths and salary insights - -If you're interested in statistics, here are a few job positions you can choose from: - -* Statistician -* Biostatistician -* Research Analyst - -**Statistician:** As a statistician, you will design and conduct statistical analysis, interpret data, and communicate findings to researchers and other stakeholders. Many statisticians work on research projects, clinical trials, and large-scale surveys, helping organizations make data-driven decisions. - -There are different levels in the statistician job profile, from applying statistical techniques for data interpretation to leading statistical research and influencing policy decisions. Strong analytical skills and a solid understanding of statistical methods are essential for this career path. - -According to [Indeed](https://www.indeed.com/career/statistician/salaries), the average salary of a statistician in the United States is $89,126 per year. - -**Biostatistician:** Biostatisticians apply statistical methods to biological and medical research, making significant contributions to the healthcare and pharmaceutical industries. If you are planning to pursue a career in this field, you will design and analyze clinical trials, ensuring the accuracy and reliability of biomedical research. This career path offers opportunities in hospitals, government agencies, research institutions, and pharmaceutical companies. - -The [average salary](https://www.indeed.com/career/biostatistician/salaries) for a biostatistician is $101,789 per year in the United States. - -**Research analyst:** In this role, you will use statistical and analytical methods to collect and interpret information to support business decisions. You will also identify trends, generate insights, and present detailed reports summarizing your research for stakeholders. - -According to [Indeed](https://www.indeed.com/career/research-analyst/salaries), the average salary for a research analyst in the United States is $75,789 per year. - -## Are data science and statistics interconnected? - -A statistician uses statistical tools and mathematical models to analyze customer data and distinct customer segments with similar characteristics. This analysis will help stakeholders understand the factors responsible for customer churn rates. Data scientists, meanwhile, will collect big data and use machine learning models to perform predictive analysis. - -Statistics answer questions like "Why did customer churn get high in the first quarter?" or "What is the reason behind the sudden downfall"? Data science, on the other hand, answers "How can this issue be fixed?". - -So yes, data science and statistics are deeply interconnected. - -Statistics provides the foundation for analyzing data, such as finding patterns and trends. Data science professionals, on the other hand, create sophisticated models and present insights through data visualization. Data science is an extended version of statistics that uses statistical methods, scientific methods, machine learning, and artificial intelligence to manage unstructured or semi-structured data. - -Data science further leverages predictive modeling and other advanced techniques to understand past events, predict future trends, identify potential risks, solve real-world problems, and develop improvement strategies. - -Additionally, statistics doesn't require computational skills but an understanding of basic mathematical models to analyze and predict results. - -## Next Step? - -Choosing between data science and statistics can be a tough call. Hopefully, this guide has helped clarify which career path is the best for you. Once you've made a decision, the next step is to get started. - -To help you with your journey, I encourage you to explore the [comprehensive resources](https://roadmap.sh/ai-data-scientist) that delve deeper into the specifics of each path. Whether you are interested in programming and solving complex datasets or theoretical analysis and working with structured data, we have great resources for each career option. - -You can apply for internships or join online courses to gain more experience or sharpen your knowledge. If you still have queries or doubts about these fields, join the [Discord community](https://roadmap.sh/discord) to stay up-to-date! \ No newline at end of file diff --git a/src/data/guides/asymptotic-notation.md b/src/data/guides/asymptotic-notation.md deleted file mode 100644 index b7db091bb..000000000 --- a/src/data/guides/asymptotic-notation.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -title: 'Asymptotic Notation' -description: 'Learn the basics of measuring the time and space complexity of algorithms' -authorId: 'kamran' -seo: - title: 'Asymptotic Notation - roadmap.sh' - description: 'Learn the basics of measuring the time and space complexity of algorithms' -isNew: false -type: 'visual' -date: 2021-04-03 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'visual-guide' - - 'guide-sitemap' ---- - -Asymptotic notation is the standard way of measuring the time and space that an algorithm will consume as the input grows. In one of my last guides, I covered "Big-O notation" and a lot of you asked for a similar one for Asymptotic notation. You can find the [previous guide here](/guides/big-o-notation). - -[![Asymptotic Notation](/guides/asymptotic-notation.png)](/guides/asymptotic-notation.png) diff --git a/src/data/guides/avoid-render-blocking-javascript-with-async-defer.md b/src/data/guides/avoid-render-blocking-javascript-with-async-defer.md deleted file mode 100644 index 23852c97d..000000000 --- a/src/data/guides/avoid-render-blocking-javascript-with-async-defer.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: 'Async and Defer Script Loading' -description: 'Learn how to avoid render blocking JavaScript using async and defer scripts.' -authorId: 'kamran' -seo: - title: 'Async and Defer Script Loading - roadmap.sh' - description: 'Learn how to avoid render blocking JavaScript using async and defer scripts.' -isNew: false -type: 'visual' -date: 2021-09-10 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'visual-guide' - - 'guide-sitemap' ---- - -[![Avoid Render Blocking with Async and Defer](/guides/avoid-render-blocking-javascript-with-async-defer.png)](/guides/avoid-render-blocking-javascript-with-async-defer.png) diff --git a/src/data/guides/backend-developer-skills.md b/src/data/guides/backend-developer-skills.md deleted file mode 100644 index dd1f92fd3..000000000 --- a/src/data/guides/backend-developer-skills.md +++ /dev/null @@ -1,275 +0,0 @@ ---- -title: '8 In-Demand Backend Developer Skills to Master' -description: 'Learn what the essential backend skills you should master to advance in your career.' -authorId: fernando -excludedBySlug: '/backend/developer-skills' -seo: - title: '8 In-Demand Backend Developer Skills to Master' - description: 'Learn what the essential backend developer skills are that you should learn and master to advance in your career.' - ogImageUrl: 'https://assets.roadmap.sh/guest/backend-developer-skills-ece68.jpg' -relatedTitle: "Other Guides" -relatedGuidesId: backend -isNew: false -type: 'textual' -date: 2024-02-27 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![The best backend developer skills to learn.](https://assets.roadmap.sh/guest/backend-developer-skills-ece68.jpg) - -Whether your goal is to become a backend developer or to stay relevant as one, the goal itself requires adopting an eternal student mindset. The ever-evolving web development space demands continuous learning, regardless of the programming language you use. New frameworks, libraries, and methodologies emerge regularly, offering different solutions to old problems. To remain relevant as a [backend developer](https://roadmap.sh/backend), you’ll have to stay updated by honing your core skills. - -In this article, we’ll cover the following set of backend developer skills we recommend you aim for: - -- Keeping an eye on core and new backend programming languages -- Understanding the basics of software design and architecture -- Understanding databases and how to use them -- API development -- The basics of version control -- Testing and debugging -- CI/CD and DevOps fundamentals -- Soft skills - -So, let's get going! - -## Understanding Backend Development - -Before we move on and start discussing the different backend development skills you should focus on, let’s first understand what a backend developer is. After all, if you’re looking to actually become a backend developer, you’ll need this. - -A backend developer focuses entirely on writing business logic for an application and much of the supporting logic as well. - -That said, there might be applications where the business logic is split into the frontend and the backend. However, while the frontend dev might have to share their time between UI code and business logic, the backend dev will focus most of their time on core business logic. That’s the main difference between the two. - -![UI vs Backend](https://assets.roadmap.sh/guest/6529303b545cb53d4aa730ca_1709056806118.png) - -In the above image, you can see how there is a lot more behind the curtain than just the UI when it comes to web applications. In this case, a “simple” log-in form needs a backend to contain its core business logic. - -Let’s now look at the most in-demand backend developer skills you should focus on in backend development. - -## Proficiency in Core and Emerging Programming Languages - -One of the most basic skills you should focus on as a backend developer is on identifying key programming languages to learn (or at least keep an eye out for). - -There are some essential backend languages that the industry has adopted as de facto standards. This means most new projects are usually coded using one (or multiple) of these programming languages. - -![core-languages](https://assets.roadmap.sh/guest/6529303b545cb53d4aa730ca_1709058292005.png) - -The most common names you should look out for are: - -- **JavaScript (or any of its variants, such as TypeScript).** This is a very common option because it’s also the language used by frontend developers, thus making it easier for developers to work on both sides of the same project. -- **Python.** While a very common option for other types of projects (such as data processing and [data science](https://roadmap.sh/ai-data-scientist)), it’s still very popular in the web development world. Python has many good qualities and supporting frameworks that make it a very easy-to-pick-up option for coding backend systems. -- **Go (A.K.A Golang).** This programming language was developed by Google. It was designed with simplicity, efficiency, and concurrency in mind. That’s made it gain popularity in the backend development space, making it an interesting option for projects that prioritize performance and concurrency. -- **Java.** One of the most common alternatives for enterprise solutions, Java, has been constantly evolving since its first release back in 1995. All that time making its way into big enterprises that trust its robustness and ever-growing community of developers. While not the easiest language to learn, it’s definitely up there in the top 10 most popular [backend languages](https://roadmap.sh/backend/languages) (according to [StackOverflow’s 2023 Developer survey](https://survey.stackoverflow.co/2023/#technology-most-popular-technologies)). - -While there are other options, the ones mentioned above, from the backend point of view, are some of the most relevant languages to pay attention to. Here are the top 10 most popular ones amongst professional developers (screenshot taken from SO’s survey of 2023): - -![StackOverflow Survey Result](https://assets.roadmap.sh/guest/6529303b545cb53d4aa730ca_1709057007054.png) - -### Keeping an eye on the rising stars - -If working with at least one of the most common backend languages was important, understanding what are the rising technologies in the backend world is just as crucial. - -You won’t see a new programming language being released every month. However, in the span of a few years, you might see the release of several, and out of those, some might stick long enough to become new standards. - -For example, take a look at the period between 2012 and 2015; in just 3 years, 9 programming languages were released, out of which most of them are being used to this day. - -![Famous Languages](https://assets.roadmap.sh/guest/6529303b545cb53d4aa730ca_1709058257292.png) - -- In 2012, we got Julia, Elm, Go, and TypeScript. -- In 2013, we got Dart -- In 2014, we got Swift, Hack, and Crystal -- And in 2015, we got Rust. - -Some of those languages are very relevant to this day, such as TypeScript and Rust, while others, such as Hack and Crystal, might be known to only a few in very niche sectors. - -Of course, it’s impossible to predict which programming language will become a standard. However, the skill that you need to hone is that of keeping an eye on the industry to spot new and emerging trends. - -### The importance of supporting frameworks - -[Frameworks](https://roadmap.sh/backend/frameworks) for a specific programming language do change a lot faster than the language itself, though. - -Frameworks are there to provide you with a simplified gateway into the functionalities that you’d normally need for common tasks. For example, in the context of backend web development, frameworks usually take care of: - -- **Parsing HTTP requests** and turning them into objects you can easily interact with (so you don’t have to learn how the HTTP protocol works). -- **Abstracting concepts,** such as a request or a response, into objects and functions that you can reason about at a higher level. This gives you an easier time thinking about how to solve a problem using these tools. -- **Accessing data becomes a lot easier when there are abstractions.** Some frameworks provide what is known as an ORM (Object Relational Mapping). Through ORM, you can interact with databases without having to think about writing SQL queries or even database schemas. -- And many more. - -Frameworks are an essential aspect of the work you’ll do as a backend developer, which is why you should not neglect them. Of course, learning and mastering every single framework out there is impossible. Instead, learn to keep an eye out in the industry and see what are the most common frameworks, and focus on one (or two) of them. - -## Software Design and Architecture - -Coding is not just about writing code. - -While that statement might be a bit confusing, the truth is there is a lot of architecture involved in software development (both in frontend and backend development). Sometimes, working on these aspects of a system is the job of a specific role called “architect.” However, for backend systems, it’s not uncommon for backend developers to also be involved in architecture conversations and decisions. You’re helping design the underlying backend infrastructure, after all. - -The following diagram shows an example of what a very simple system’s architecture might look like: - -![Simple System Architecture](https://assets.roadmap.sh/guest/6529303b545cb53d4aa730ca_1709057266440.png) - -While the example is oversimplified, it gives you an idea of what the practice of “architecting a system” is. - -Essentially, architecting a system means coming up with concepts that represent different aspects of the solution and then deciding how you want to make them interact with each other. - -Why is architecture so important here? Because it gives you properties such as code encapsulation, separation of concerns, reusability, and even scalability as a byproduct of the architecture itself. - -Let’s take a quick look at some of the most common architectures used for creating backend systems. - -### Most common backend architectures - -There are too many different architectural styles and patterns to cover them all inside a single article, but let's just look at some of the most common ones and how they can help you while working on your backend system. - -- **Monolithic architecture:** In a monolithic architecture, the entire application is built as a single, tightly coupled unit. All components (frontend, backend, database, etc) are part of the same codebase. This is a great first architecture because it feels very natural to develop under, and if your project is not going to grow out of proportion, then you will probably not hit any of the cons. -- **Microservice-based architecture:** The application is divided into small, independent services, each responsible for a specific business capability. These services communicate through APIs. -- **Service-Oriented Architecture:** Similar to microservices, a service-oriented architecture splits functionality into individual services. The main difference is that these services aren’t as granular as a microservice, so they might incorporate functionalities related to different business entities. -- **Event-driven architecture:** With this architecture, each system (or service) responds to events (e.g., user actions and messages from other services) by triggering actions or processes. All services communicate with each other indirectly through an event bus (also known as a “message bus”), so it removes the possibility of having two or more services coupled with each other (meaning that they can’t be treated individually). -- **Serverless Architecture:** Also known as Function as a Service (FaaS), serverless architecture allows you to focus on writing code without worrying about the server where they’ll run. Functions are executed in response to events without the need for provisioning or managing servers (this is done FOR you automatically). -- **Microkernel architecture:** This architecture lets you build the core, essential functionality into a small microkernel and have the rest of the features built as plugins that can be added, removed or exchanged easily. - -And if you want to know more about the patterns and principles mentioned here, please check out the [Software Design and Architecture roadmap](https://roadmap.sh/software-design-architecture). - -## Mastery of Database Management Systems - -As a backend developer, you will undoubtedly have to deal with database administration in your daily tasks. They are the industry standard for storing persistent data. - -Because of that, it’s important to understand that you should be aware of two main categories of databases: SQL databases and NoSQL databases. - -### SQL databases - -These are the standard structured databases (A.K.A relational databases) where you need to define the schema for your data (essentially the data structures you’re dealing with), and then you’ll use a language called [SQL (Structured Query Language)](https://roadmap.sh/sql) to interact with the data inside it. Most backend developers will interact with SQL databases at some point in their career, as this is the most common type of database. - -### NoSQL databases - -As the name implies, these are not your standard SQL databases; in fact, within this category, there are columnar databases, document-based ones (such as [MongoDB](https://roadmap.sh/mongodb)), key-value-based ones (like [Redis](https://roadmap.sh/redis)), and more. They don’t use predefined data structures, giving you more flexibility and control over what you can store and how you store it. Backend developers will deal with only a handful of these, as there are many different sub-types, and more are created every year. - -Some examples of these databases are: - -- MongoDB, a document-based database (see here a mongoDB roadmap if you’re interested). -- Redis, an in-memory key-value pair database. -- Neo4J, a graph database. -- ElasticSearch, a document-based search engine. - -In the end, the decision between SQL and NoSQL is about trade-offs and figuring out what works best for your particular use case. - -## API Development Capabilities - -Application Programming Interfaces (APIs) are everywhere. They power the backend of almost all major systems out there (according to a [study conducted by O’Reilly in 2020](https://www.oreilly.com/pub/pr/3307), 77% of companies were using microservices/APIs). - -That is to say, if you’re thinking about becoming a backend developer, you will be coding APIs/microservices. This is why understanding the basics of them is crucial to ensuring your relevance in the field. - -![System vs External System](https://assets.roadmap.sh/guest/6529303b545cb53d4aa730ca_1709057608824.png) - -The above diagram explains how APIs interact with whatever you might be building. - -Now, if you’re inside the “**The System**” box, then you need to understand how to interact with these APIs using the right tools. If you’re inside the “**External System**” box, then you need to understand the type of standards these APIs need to follow and how to implement them. - -Don’t worry though, for both situations, there are always frameworks and libraries you can use to simplify your task and ensure you’re following the proper industry standards. - -### What are the most common API types? - -The most common types of APIs used in the industry currently are REST and GraphQL. - -As a backend developer, it’s not mandatory that you master both of these types, but it’s definitely recommended that you have some practical experience with one of them. - -- **RESTful APIs.** These are APIs that work over HTTP and make extensive use of the HTTP Verbs to give meaning to each request. They’ve been the most popular type of API until recently, so there are still a lot of projects and teams that make use of it. -- **GraphQL.** GraphQL APIs operate over HTTP as well, leveraging the HTTP protocol and its verbs. In contrast to the conventional RESTful APIs, GraphQL has emerged as a powerful alternative, offering a flexible and efficient approach to data querying and manipulation. GraphQL allows clients to request only the data they need, providing a more tailored and efficient interaction between clients and servers. - -Is there one better than the other? There is no easy way to answer that question as both are capable of doing everything you’d need. It’s more about your particular requirements and the preferences of your dev team. - -## Version Control Savvy - -One mandatory skill that all backend developers should work on (actually, all developers, in general) is version control, or in other words, understanding and being familiar with version control systems. - -Essentially, you’ll want to know how to use the version control tool that everyone else is using. The industry standard at the moment of writing this is [Git](https://git-scm.com/), while there might be some teams using other (older) tools, as long as you understand the current one, you’ll be in good shape. - -### What is version control? - -Version control references the ability for you and other developers to share code with each other while you’re working on the same files. - -While Git is the industry standard at the moment, GitHub has created such a popular platform around Git, that it almost makes it mandatory to learn about. - -So go ahead and create an account, browse what others are doing, and upload your own personal projects. It’s definitely a great way to learn. - -### What should you learn about Git? - -If you’re picking up Git as your version control system of choice, there are two main areas you should be focusing on. - -- **The basics.** Clearly understanding how Git works and the basic commands to add, push and pull changes. You should aim to learn enough about them to feel comfortable using them on your day-to-day (because you will). -- **Branching strategies.** Sadly, using Git alone is not enough. While through Git you can already start versioning your code, when the project is complex enough and your team big enough, the tool alone will not be enough. You’ll have to come up with [branching strategies](https://learngitbranching.js.org/?locale=es_ES) to organize the whole team’s workflow. - -Keep in mind that Git and Git branching are not trivial topics, and they’ll take a while to master. So, while you should give yourself time to learn about them, also make sure you check with others (or use tools such as ChatGPT) to validate your commands before using them. Remember, a wrong Git command or a wrong workflow can cause major problems within a project, especially if there are many developers working on the same codebase. - -## Testing - -Understanding both what testing is and the importance of it within the backend development workflow is crucial for all developers, and one of the mandatory backend developer skills to focus on. - -Testing is the development process of making sure your code works in a way that doesn’t involve you manually testing every feature but rather using tools that allow you to test and reproduce any problems that can be found programmatically. - -This, of course, helps to remove potential human error from the equation when testing big systems and to increase the speed at which these tests can be done (think seconds vs hours of you doing it manually). - -Testing is a far more complex discipline than I can describe here. Just know that there are many different ways to test a system, and all backend developers should be aware of the following: - -- **Unit testing:** This is the most common way of doing code testing. You’ll write tests using a testing framework for every publicly available function/method in your code. This way you’re making sure every piece of code that can be used is tested and performs according to plan. Running these tests is usually quite fast, so you’ll be doing it before every commit (usually). -- **Integration testing:** If you’re building a system that consists of multiple independent systems working together (think, for instance, a microservice-based architecture), then testing each individual part is not enough. You also have to make sure systems that should interact with each other do so correctly. This is where integration tests come into play. -- **End-to-end testing (E2E):** These tests are similar to integration tests, but they also include the UI of the system. There are tools you can use to automate actions in your UI as if a real user were performing them and then checking the result. For example, clicking on a log-out button and checking if you’re later redirected to the log-in screen. This flow would involve the backend performing some actions that result in the user being logged out. -- **Load testing:** While not exactly the same process as with the previous test types, load testing is great for backend systems because it helps you determine if your backend is ready to deal with high amounts of traffic. - -You can think of the list in graphical format as the following diagram: - -![Testing types](https://assets.roadmap.sh/guest/6529303b545cb53d4aa730ca_1709057834295.png) - -If you’re just getting started with testing, I’d recommend focusing only on unit testing for the time being. Once you have a grasp on it, start moving out following the above diagram and slowly move into the other types as you progress. - -## CI/CD and DevOps Familiarity - -As a backend developer, your code will be constantly deployed, either into cloud environments or perhaps even into normal, on-premise servers. The point is that what you build will run through CI/CD (Continuous Integration and Continuous Deployment) processes. - -![ci-cd](https://assets.roadmap.sh/guest/6529303b545cb53d4aa730ca_1709058122541.png) - -These processes will automatically test it (Continuous Integration) and automatically deploy it (if all tests go well). As a backend developer, you’re not usually expected to know and understand how to configure these processes; however, it’s important that you know about them. - -DevOps is yet another tangential area to that of a backend developer. When teams are small enough, backend devs might be “gently pushed” into tasks such as configuring CI/CD pipelines, setting up servers, and more. These tasks are usually performed by dedicated professionals with the role of DevOps. Their specialty is automation, making the deployment process efficient and ensuring that everything runs smoothly in the real-world server environment. They play a crucial role in maintaining the reliability and performance of applications and websites. - -So, while they’re not strictly the responsibilities of backend developers, they’re close enough to the role’s day-to-day that it would be a good idea to learn about them. If you’re interested in learning more about DevOps, check out [our DevOps roadmap](https://roadmap.sh/devops) containing all the key topics you should learn about if you want to become a DevOps engineer. - -## Soft Skills - -Finally, the last set of backend developer skills you should focus on are, actually, not technical skills, nor are they exclusively useful for backend developers. These are skills that every developer should work on during their career: soft skills. - -### Improving communication - -The ability to communicate with others, both technical and non-technical people, is crucial in any developer's career. - -For backend developers, it’s especially important because communicating their work and the effects of it is definitely harder than other roles, such as frontend developers who can actually showcase what they’re building. - -As a backend developer, you’ll be able to better explain problems or blockers to your colleagues, you’ll be able to perform requirement gathering much more effectively, and you’ll even improve your own problem-solving skills by being better at articulating the problems and potential solutions to yourself. - -### Critical thinking - -Honing your critical thinking as a backend developer will help your ability to analyze complex problems, identify patterns much faster, and come up with innovative solutions to the problems you’re facing. - -Pushing the limits of your critical thinking skills will also foster a more systematic and strategic approach to coding and architecting robust and efficient solutions. - -In other words, it’ll make you a better and more efficient coder. And who doesn’t want that? - -## Conclusion - -To summarize, if you expect to become a backend developer or to grow in the area of backend development: - -- Keep an eye on the industry to understand what’s the status quo and what’s new and hot. -- Understand the basics of software design and architecture. -- Look into relational databases and NoSQL databases as well; they’re both important. -- Learn how to build and use APIs; they’ll be part of almost every project you work on. -- Remember, testing might look like it’s not mandatory, but it’s definitely a standard practice when it comes to backend development. -- CI/CD and DevOps are practices you’ll be involved with, either directly or indirectly, so learn about them. -- Soft skills are just as important as technical skills if you expect to grow in your career. - -That said, do not take this list as the ultimate roadmap but rather as a starting point. If you’re willing to take your backend developer career to the next level, push yourself out of your comfort zone and pursue the skills listed here and the ones listed in this detailed [backend roadmap](https://roadmap.sh/backend). - -Remember, constant learning is the only absolute truth in the software development world (this is true for backend developers, too). If you keep your skillset updated with the latest trends, you’ll remain adaptable and effective as a backend developer. diff --git a/src/data/guides/backend-developer-tools.md b/src/data/guides/backend-developer-tools.md deleted file mode 100644 index 21ba34d18..000000000 --- a/src/data/guides/backend-developer-tools.md +++ /dev/null @@ -1,391 +0,0 @@ ---- -title: '25 Essential Backend Development Tools for @currentYear@' -description: 'Elevate your development process with these 25 essential backend developer tools.' -authorId: fernando -excludedBySlug: '/backend/developer-tools' -seo: - title: '25 Essential Backend Development Tools for @currentYear@' - description: 'Elevate your coding with backend developer tools that bring efficiency, scalability, and innovation to your projects. Improve your development process today!' - ogImageUrl: 'https://assets.roadmap.sh/guest/backend-development-tools-ou6el.jpg' -relatedTitle: "Other Guides" -relatedGuidesId: backend -isNew: false -type: 'textual' -date: 2024-03-19 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Must-have backend developer tools to use.](https://assets.roadmap.sh/guest/backend-development-tools-ou6el.jpg) - -As developers, we’re not just writing code on a text editor without any other external help. Whether we realize it or not, we’re constantly using different development tools to improve the way we work and the speed at which we can deliver our code. - -In this article, we’ll cover 25 backend development tools that are crucial in the web development industry, and as a [backend developer](https://roadmap.sh/backend), you should be aware of them. - -The categories we’ll tackle are: - -- IDEs and editors -- Database tools -- Collaboration -- Hosting Services -- API-Related tools -- Productivity tools - -So let’s get started! - -## Beyond programming languages: IDEs and Editors - -Other than the actual programming languages, the Integrated Development Environment (A.K.A your IDE) is the single most important tool you’ll have to pick and use throughout your career in software development. - -Some of them are generic (as in, they work for all types of development), and others will have specific backend development tools (which is what we’re focusing on right now). - -Let’s see some examples that are great for a web development project. - -### 1. Jetbrains Products - -The [Jetbrains family](https://www.jetbrains.com/) of IDEs targets multiple programming languages, including JavaScript, .NET, JAVA (and the Java Virtual Machine), Python, PHP, and more (mostly great options for web development). - -![Jetbrains code editor](https://assets.roadmap.sh/guest/jetbrains-code-editor-z7z1t.png) - -The benefit of using these IDEs, in comparison with others, is that given how they’re language/technology specific, they have tools designed for those programming languages and specifically for this list to help in your backend development tasks, such as: - -- Debuggers. -- Improved IntelliSense. -- Improved development environment. - -The only minor issue with these IDEs, especially when compared to the rest of the options listed here, is that they’re not all free. While not all of them are priced the same, I recommend you check out your IDE’s pricing page to understand what options you have (there are free plans if you qualify for them). - -### 2. Visual Studio Code - -[VSCode](https://code.visualstudio.com/) is definitely one of the most popular alternatives these days for all types of web developers, but definitely for backend developers. This IDE’s strongest selling point is that it’s incredibly extensible through plugins. And the community using it is so big and varied that there are plugins for literally anything you need. - -![VSCode code editor](https://assets.roadmap.sh/guest/vscode-code-editor-8pznw.png) - -The other major benefit of VSCode over JetBrains products is that it gives developers a fully working IDE for FREE. While some of the extensions don’t provide exactly the same developer experience as a JetBrains IDE, the proper combination of extensions can provide a very close alternative through VSCode. - -### 3. Zed - -[Zed](https://zed.dev/) is a different type of code editor, and because of that, it might just be the right one for you. - -![Zed code editor](https://assets.roadmap.sh/guest/zed-code-editor-7ovxz.png) - -Zed, like VSCode, is an all-purpose code editor, letting you code in whatever language you want (whether you’re doing web development or not). The main reasons why you’d pick Zed over others are: - -- **Improved performance.** Zed takes advantage of your CPU AND GPU to improve the speed at which the IDE responds to your commands. -- **Multi-user support.** -- **Team features.** Zed lets you build software while working with others by sharing notes and letting you interact with teammates through the IDE. - -Zed is an [open-source project](https://github.com/zed-industries/zed), but at the time of writing this article, it’s only available for macOS, so Linux and Windows users are still unable to try this excellent option. - -### 4. Sublime Text - -Before VSCode, [Sublime Text](https://www.sublimetext.com/) was probably one of the most popular code editors for web developers who wanted something powerful for free. - -Just like VSCode, Sublime supports extensibility through plugins, and the rich ecosystem of plugins makes it quite a versatile editor. As a note, this code editor also supports GPU rendering of the UI, like Zed does, so if performance is important to you, then you’ll want to keep reading. - -![Sublime code editor](https://assets.roadmap.sh/guest/sublime-text-code-editor-ngtnf.png) - -With a refreshed UI (if you’ve used Sublime Text in the past, you’ll be happily surprised!) and better internal tooling, the latest version of this editor (version 4) is trying to regain the portion of the market that VSCode took from it at the time. - -### 5. VIM - a developer-focused editor - -[VIM](https://www.vim.org/) is a tool that you either hate or love as a developer, but there is no middle ground. - -This is such an iconic text editor that all the previously mentioned IDE have what is called a “vim mode,” which allows you to use them as if you were using VIM (with the visual and input modes). - -![Vim code editor](https://assets.roadmap.sh/guest/vim-code-editor-ohiyc.png) - -Vim lets you write code without having to move your fingers away from the home row (the row where you “rest” your fingers, the one with the F and G keys). That means you can navigate documents, write code, move through projects, and more, all with minimum hand movement. - -This is the key philosophy behind Vim’s design, and if you embrace it, it should help to make you a very proficient developer. Of course, adapting to this way of working is not trivial, and there is a lot of muscle memory that has to be re-trained. But once you do it, it’s really hard to go back. - -Just like with all the generic IDEs here, you’ll have to [customize it through “scripts”](https://www.vim.org/scripts/script_search_results.php?order_by=creation_date&direction=descending) to make it work exactly as you want for your environment. - -## Database Tools - -While doing backend development, you will definitely be interacting with databases. They’re a ubiquitous backend tool in the realm of web development. - -Let’s take a look at some great database tools you can use as a backend developer to interact with your favorite database management systems (DBMS). - -### 6. DataGrip - -[Datagrip](https://www.jetbrains.com/datagrip/) is a JetBrains product, which makes it a great option if you’re also going with a JetBrains IDE. - -This tool lets you access all SQL databases from within the same user interface, it provides great help while browsing the data stored in the database, and it also has features that help you write better SQL queries. - -![DataGrip database explorer](https://assets.roadmap.sh/guest/datagrip-database-explorer-l8987.png) - -While the pricing of these tools might be a bit steep (especially if you go with the IDE as well), it’s definitely a solid option if you’re looking for feature-rich and stable software development tools. - -### 7. Navicat - -[Navicat](https://navicat.com/en/products) actually has a family of alternatives based on what you need, from the standard set of SQL databases (such as MySQL, Oracle, Postgre, and so on) up to other NoSQL databases such as MongoDB and Redis. - -In general, the Navicat alternatives are quite lightweight and powerful to use. They might not be as feature-rich as Datagrip, but they let you easily browse and query the data you need. - -![Navicat database explorer](https://assets.roadmap.sh/guest/navicat-database-explorer-r1unn.png) - -The free trial for Navicat only lasts 14 days, and then you’ll have to pay a monthly fee. That said, for non-commercial use, the license is quite low, which makes it accessible to almost all developers. - -As for features, it has all the ones you’d expect from a tool like this: - -- Ability to connect to multiple databases and visually browse their content. -- Query editor with some IntelliSense built-in. -- Model representation (automatically converts a database into ER Diagrams). -- Simple object designer to create entities through a visual IDE. - -The Navicat series of apps are great backend tools to have at your disposal, if you don’t mind their price, that is. - -### 8. TablePlus - -[Tableplus](https://tableplus.com/) is very similar to Navicat in the sense that it’s another lightweight database manager. The main differences are: - -- Tableplus only supports SQL-based databases. -- The pricing model is simpler, by only charging a one-time license without you having to commit to a monthly subscription. - -![TablePlus database manager](https://assets.roadmap.sh/guest/tableplus-database-manager-06d09.png) - -Some of the most relevant features of Tableplus are: - -- Inline data editing. -- Advanced filtering lets you combine different filtering conditions when searching. -- Code auto-complete, which comes in very handy when writing SQL queries. -- Extensible through JavaScript plugins (currently in Beta). - -This is a solid backend tool alternative to any of the previous options and with a simpler price tag. - -### 9. DBeaver - -[DBeaver](https://dbeaver.io/) is a free, cross-platform tool that lets you connect and interact with multiple databases. While there is a PRO version with extra features, the free version is more than powerful enough to get you started with almost any database you can think of, both SQL and NoSQL alike. - -For a full list of supported databases on the free version, check out their [about page](https://dbeaver.io/about/). - -![DBeaver database manager](https://assets.roadmap.sh/guest/dbeaver-database-manager-fvlrd.png) - -Some of the major features of DBeaver are: - -- SQL editor with completion. -- ER-diagram creation from a table definition. -- In-line data editing. - -Task management to kill any long-lasting queries that block your database. - -## Collaboration tools for web development - -Unless you’re working as a solo-dev (and even then!), collaboration tools allow you to coordinate your work and understand who’s working on what and what you should be working on next. - -While these might not be considered “backend tools” per se, they definitely help improve your performance and organization, so we can still call them “development tools” as a broader term. - -### 10. Trello - -[Trello](https://trello.com/) is a very simple yet powerful organizational tool that lets teams build a Kanban-like board with clear states and simple UX (drag&drop is king in Trello). - -Setting up a new project and a team takes minutes in Trello, and through the plugin system, you can get extra features such as date reminders, calendar integrations, and more. - -![Trello board](https://assets.roadmap.sh/guest/trello-board-o0i4i.png) - -The simple UI and intuitive UX make Trello one of the best options out there for collaborative tools as long as the needs of the team are met with simple column-based layouts and minimal information. - -### 11. Monday - -[Monday](https://monday.com/) is a relatively new online platform for project management and collaboration. I say “new” because some of the other alternatives here have been around for over 5+ years. - -Their limited free plan lasts forever, so if you have a small team and limited requirements, this might just be the perfect tool for you. Also, if you actually need to pay, Monday’s plans are accessible, especially when compared to other alternatives. - -![Monday project management tool](https://assets.roadmap.sh/guest/monday-project-management-tool-xws17.png) - -Monday’s fully customizable UI lets you build the collaborative environment you need. This is a huge advantage over others who've been around for longer and have a fixed UI that you have to adapt to. - -### 12. Basecamp - -[Basecamp](https://basecamp.com/) is a mix between Trello, Monday, and Notion in the sense that it tries to provide developers with the best and most relevant tools from those worlds, leaving out the ones that just create “noise.” - -![Basecamp project management tool](https://assets.roadmap.sh/guest/basecamp-project-management-tool-2pusd.png) - -Basecamp’s philosophy is to keep things simple and only focus on the features that truly add to collaboration: - -- Simple card tables like Trello. -- Ability to upload and manage documents and files with your team. -- Integrated chat. -- Message boards to send notifications to everyone. - -The only “downside” to basecamp, if you will, is that there is no “forever free” plan. Both their plans are paid and have a 30-day free trial, so you can definitely give it a shot and figure out if what they offer is enough for your needs. - -### 13. Wrike - -[Wrike](https://www.wrike.com/) is yet another attempt at making project management and collaboration feel organic and seamless. They have a minimalistic UI and provide you with over 400 integrations to create your own workflows based on your needs and current ecosystem. - -They have a free plan that, while feature-limited, it’s perfect for understanding the basic way of using Wrike and how useful it can be to you in your current project. - -![Wrike project management tool](https://assets.roadmap.sh/guest/wrike-project-management-tool-5g3kl.png) - -Their innovative use of AI allows you to create content faster, analyze project and task descriptions, and create subtasks based on it. - -Wrike feels efficient and powerful, even for its free plan. Give it a chance if you’re a freelancer or if you have a small team looking for something new and powerful. - -## Hosting Services - -When it comes to backend development, deploying your code and running it on the cloud will be a common thing; this is a practice known as continuous integration and continuous deployment (CI/CD). While in some situations, you’ll be dealing with a custom, in-house infrastructure, there are platforms that will make the entire process very lightweight (as in deploying with a couple of clicks). - -Let’s take a look at some of the most common alternatives! - -### 14. Railway - -[Railway.app](https://railway.app/) aims at giving developers all the tools they need at a click’s distance. We’re talking about: - -- PR-triggered deployments. -- Support for all popular programming languages. -- Autoscaling. -- Load balancing. -- Monitoring. -- A great uptime (99.95%) -- With more than 200 ready-made templates for you to get going. - -![Railway hosting](https://assets.roadmap.sh/guest/railway-hosting-j8me8.png) - -Railway has no free plan, but their basic one is very accessible. Careful though, they also charge per resource utilization. Lucky for you, they have a [very handy consumption calculator](https://railway.app/pricing) to avoid surprises at the end of the month! - -### 15. Heroku - -[Heroku](https://www.heroku.com/) is another Platform as a Service provider. This one provides the basic services most of them do, such as autoscaling, monitoring, GitHub integration, and more. - -The list of supported programming languages is not huge, but the most common ones are definitely covered: Node.js, Ruby, JAVA, PHP, Python, Go, Scala, and even Clojure. - -![Heroku hosting](https://assets.roadmap.sh/guest/heroku-hosting-2u6bz.png) - -Another great selling point for Heroku is that on top of their infrastructure, they also offer a managed Postgre database as a service and a Redis one. In both situations, you’ll have to pay for the services as you use them, so keep that in mind. - -### 16. Digital Ocean - -As opposed to platforms such as Heroku, [Digital Ocean](https://www.digitalocean.com/) is known as an Infrastructure as a Service provider (IaaS). They give you all the servers you need and all the resources (memory, CPU, etc) you want to pay for. However, setting up your deployment process, automating your integration tests, or even having all the required libraries to run your code is up to you. - -This is by no means something bad, some teams do prefer to have that freedom over other platforms like Railway and Heroku, where everything’s already managed. - -![Digital Ocean hosting](https://assets.roadmap.sh/guest/digital-ocean-hosting-i7a9c.png) - -Large-scale applications will usually require to have custom infrastructure that managed services can hardly provide. This is where IaaS providers come in. - -On top of their basic offering, they do offer managed databases such as MongoDB, MySQL, Redis, and others. - -### 17. Hetzner - -[Hetzner](https://www.hetzner.com/) is yet another IaaS that offers everything you need to get going if you know what to do with it. In other words, they offer all the hardware you might dream of, even in the cloud, but you have to configure it and maintain it. - -Their only “managed” offer is for web hosting though, so if you’re looking to host your website or app and you don’t want to have to deal with server maintenance and configuration, then this is a good option for you. - -![Hetzner hosting](https://assets.roadmap.sh/guest/hetzner-hosting-oupq8.jpg) - -Other than that, their offering is quite standard, although their pricing model might not be. While they do have the standard pricing tiers like the rest of them, they also give you the option to “bid” for used hardware that is no longer needed. - -### 18. Vercel - -If you’re building a NextJS application and you’re looking for a quick way to deploy it, then there is probably no better place than [Vercel](https://vercel.com/) (the owner of NextJS). - -Their platform allows you to link your GitHub account to their systems and deploy your entire application with a single push to the repo. - -![Vercel hosting](https://assets.roadmap.sh/guest/vercel-hosting-9jvk9.png) - -And since they’re experts on NextJS, your app will “just work.” - -Even their free plan is perfect for quick SaaS prototypes and small applications. On top of this, they offer monitoring, auto-scaling, load balancing, and everything you’d expect from a PaaS provider. - -While it’s true they don’t support other technologies or even offer other related services, such as managed databases, there is hardly anyone who can provide a better developer experience when it comes to deploying a NextJS application. - -### 19. Render - -You can think of [Render](https://render.com/) as if Vercel and Heroku had a love child. Render gives you the amazing developer experience provided by Vercel but the flexibility (or more) from Heroku. - -You’re not tied to a single technology; instead, you have all the major runtimes available out of the box. Much higher HTTP timeouts (up to 100 minutes, which is incredible compared to the standard 10 or 30 seconds most providers give you) and tons of other security and quality-of-life improvements. - -![Render hosting](https://assets.roadmap.sh/guest/render-hosting-w4urq.png) - -Render also offers managed MySQL and managed Redis instances for you to use, even in their free tier. In the end, unless you’ve been using Heroku for a while and you’re happy with their DX, it might be a good idea to check out Render instead. - -### 20. OVHCloud - -[OVHCloud](https://www.ovhcloud.com/) is an all-in-one solution that seems to provide you with everything you need, from “bare metal” (as in infrastructure) to managed hosting for web applications, managed databases (they have many to choose from), and many other services. - -However, they do not seem to offer quality-of-life integrations to make your deployment workflow simple and intuitive. - -![OVHCloud hosting](https://assets.roadmap.sh/guest/ovhcloud-hosting-ncfch.png) - -Now, given how they centralize all related services from domain name registration all the way up to analytics, identity management, file storage (CDN), and even one-click install CMS (content management systems, such as WordPress), etc, it might just be a good option for you. That is if you have the expertise in your team to deal with all these options. - -## API-Related Tools - -As backend developers, we’ll always be dealing with APIs (Application Programming Interface), either through using the ones created by others or writing our own. - -Whatever you’re doing, it’s always good to have some backend tools to help you build and test them faster, so let’s take a look at a few options. - -### 21. Swagger - -Some developers would argue that one of the hardest parts of creating an API is documenting it. Not only because it might sound like a boring task, but explaining what the API endpoint is doing well enough is not trivial. - -That’s where [Swagger](https://swagger.io/) comes into play. - -![Swagger API tool](https://assets.roadmap.sh/guest/swagger-api-tool-vngxu.png) - -This tool allows you to create interactive documentation that provides developers with all they need to understand how to use your endpoints, and at the same time, it also gives them the option to test them directly from the generated UI. - -### 22. Postman - -[Postman](https://www.postman.com/) is less of a documentation-only app and has grown over the years to become a testing API tool that every developer and development team should know about. Backend developers are especially benefited from using Postman because of how well it helps organize and manage APIs. - -With Postman, you can organize all your company’s APIs, share them with the associated dev teams, and let them use and interact with them without having to write a single line of code. - -![Postman documentation tool](https://assets.roadmap.sh/guest/postman-documentation-tool-cl81q.png) - -While Swagger is more of a development tool that every backend developer should know about, Postman is the tool that every development team should use to share & control internal API access and documentation. - -## Productivity - -Finally, the last category is about productivity. While some of the products and services mentioned already do provide productivity enhancements, they weren’t there for that. The following list of backend tools is created thinking only about the benefits they can bring to your productivity while working as a backend developer. - -### 23. iTerm - -If you’re a macOS user, then [iTerm](https://iterm2.com/) is definitely one of the “must haves” you need to look into. As a backend developer, you’ll spend a lot of your day in the terminal. - -![iTerm terminal developer tool](https://assets.roadmap.sh/guest/iterm-terminal-developer-tool-zze25.png) - -iTerm will take that experience to the next level by bringing in features such as: - -- Parallel panes inside the same window making it easy to multi-task. -- Improved auto-complete -- In-window search outside of the current program you’re using. -- Instant replay lets you review the latest content on the screen before cleaning it. -- Paste history, letting you move through the latest pasted content into the terminal. - -Mind you, none of these features are mandatory; you can easily work without them, but they do improve your quality of life as a developer. Hence the reason why iTerm leads this list. - -### 24. Zsh/OhMyZsh - -The combination of these two gives your terminal superpowers. [Zsh](https://zsh.sourceforge.io/) is an improved shell that lets you work much faster and more efficiently if you’re spending several hours typing commands in your terminal. For example, you get features such as: - -- Advanced tab auto-complete -- Extensibility -- Spelling corrections -- And more. - -![Oh my Zsh shell](https://assets.roadmap.sh/guest/zsh-shell-ui-6s163.png) - -As mentioned above, after you have your ZSH installed and set up, you should look into installing oh-my-zsh, which helps with configuring all the customization options you have on this shell: - -- It comes bundled with [over 300 plugins](https://github.com/ohmyzsh/ohmyzsh/wiki/Plugins), ranging from 1password integrations to the “jump” plugin, which lets you move around the filesystem by moving from mark to mark (you can assign marks to folders). -- [Plenty of themes](https://github.com/ohmyzsh/ohmyzsh/wiki/Themes) out of the box. - -If you find yourself spending hours on the terminal, consider installing this combo. - -### 25. Raycast - -[Raycast](https://www.raycast.com/) allows you to improve your productivity by giving you a better application launcher. Instead of using the default launcher, you can replace it with Raycast and gain superpowers. - -Now, you suddenly have access to hundreds of [community-created extensions](https://www.raycast.com/store) that allow you to directly interact with chatGPT from the app launcher, use GitHub, interact with VSCode directly, and more. - -![Raycast app launcher](https://assets.roadmap.sh/guest/raycast-app-launcher-w5rg3.png) - -While it is only available for macOS users, Raycast has become a must-have application for backend developers on this platform. In the end, the faster you can reach for your tools, the more productive you become. And a properly configured Raycast can make your web development process feel like a breeze. diff --git a/src/data/guides/backend-frameworks.md b/src/data/guides/backend-frameworks.md deleted file mode 100644 index 5270d4a27..000000000 --- a/src/data/guides/backend-frameworks.md +++ /dev/null @@ -1,443 +0,0 @@ ---- -title: 'Top 7 Backend Frameworks to Use in 2025: Pro Advice' -description: 'Get expert advice on backend frameworks for 2024. Learn about the top 7 frameworks that can elevate your development process.' -authorId: fernando -excludedBySlug: '/backend/frameworks' -seo: - title: 'Top 7 Backend Frameworks to Use in 2025: Pro Advice' - description: 'Get expert advice on backend frameworks for 2024. Learn about the top 7 frameworks that can elevate your development process.' - ogImageUrl: 'https://assets.roadmap.sh/guest/top-backend-frameworks-jfpux.jpg' -relatedTitle: "Other Guides" -relatedGuidesId: backend -isNew: false -type: 'textual' -date: 2024-09-27 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Best backend frameworks](https://assets.roadmap.sh/guest/top-backend-frameworks-jfpux.jpg) - -Choosing the right backend framework in 2025 can be crucial when you’re building web applications. While the programming language you pick is important, the backend framework you go with will help define how scalable, secure, and maintainable your application is. It’s the foundation that supports all the features your users interact with on the frontend and keeps everything running smoothly behind the scenes. - -So, it’s a decision you want to get right. - -In 2024, [backend development](https://roadmap.sh/backend) is more complex and interconnected than ever. Developers are working with APIs, microservices, cloud-native architectures, and ensuring high availability while keeping security at the forefront. It’s an era where the demand for real-time data, seamless integrations, and efficient performance is higher than ever. - -Whether you're building an enterprise-level application or a small startup, the right backend framework can save you time and headaches down the road. - -Let’s take a look at the following top backend development frameworks at the top of all lists for 2024: - -* NextJS -* Fastify -* SvelteKit -* Ruby on Rails -* Laravel -* Phoenix -* Actix - -## Criteria for Evaluating The Top Backend Frameworks - -How can you determine what “best backend framework” means for you? To answer that question, I’ll define a set of key factors to consider. Let’s break down the most important criteria that will help you make the best choice for your project: - -**Performance**: - -* A high-performing backend framework processes server-side tasks (e.g., database queries, user sessions, real-time data) quickly and efficiently. -* Faster processing improves user experience, especially in 2025 when speed is critical. - -**Scalability**: - -* The framework should handle increased traffic, larger datasets, and feature expansion without issues. -* It should smoothly scale for both small and large user bases. - -**Flexibility**: - -* A flexible framework adapts to new business or technical requirements. -* It should support various project types without locking you into a specific structure. - -**Community and Ecosystem**: - -* A strong community provides support through tutorials, forums, and third-party tools. -* A good ecosystem includes useful plugins and integrations for popular services or databases. - -**Learning Curve**: - -* An easy-to-learn framework boosts productivity and helps you get up to speed quickly. -* A framework should balance ease of learning with powerful functionality. - -**Security**: - -* A reliable framework includes built-in security features to protect user data and prevent vulnerabilities. -* It should help you comply with regulations and address security concerns from the start. - -**Future-Proofing**: - -* Choose a framework with a history of updates, a clear development roadmap, and a growing community. -* A future-proof framework ensures long-term support and relevance. - -### My go-to backend framework - -My favorite backend framework is Next.js because it has the highest scores from the group. - -That said, I’ve applied the above criteria to the best backend development frameworks I’m covering below in this guide. This table gives you a snapshot view of how they all compare according to my ratings, and I’ll explain the details further below. - -![backend frameworks](https://assets.roadmap.sh/guest/backend-framework-table-nl1iw.png) - -Of course, Next.js is the best one for me, and that works for me alone. You have to consider your own projects and your own context to understand what the best choice for you would be. - -Let’s get into the selection and what their strengths and weaknesses are to help you select the right one for you. - -## Top 7 Backend Frameworks in 2025 - -### Next.js - -![NextJS](https://assets.roadmap.sh/guest/logo-nextjs-mbn1n.png) - -Next.js is a full-stack React framework and one of the most popular backend frameworks in the JavaScript community. Over the years, it has evolved into a robust web development solution that supports static site generation (SSG), server-side rendering (SSR), and even edge computing. Backed by Vercel, it’s now one of the go-to frameworks for modern web development. - -#### 1\. Performance - -Next.js has a wonderful performance thanks to its ability to optimize for both static and dynamic generation. With server-side rendering and support for edge computing, it's built to handle high-performance requirements. Additionally, automatic code splitting ensures only the necessary parts of the app are loaded, reducing load times. - -⭐ **Rating: 5/5** - -#### 2\. Scalability - -Next.js is designed to scale easily, from small static websites to large-scale dynamic applications. Its ability to turn backend routes into serverless functions puts it at an unfair advantage over other frameworks. Paired with Vercel’s deployment platform, scaling becomes almost effortless. - -⭐ **Rating: 5/5** - -#### 3\. Flexibility - -Next.js is one of the most flexible frameworks out there. It supports a wide range of use cases, from simple static websites to highly complex full-stack applications. With its API routes feature, developers can create powerful backends, making Next.js suitable for both frontend and backend development in a single framework. - -⭐ **Rating: 5/5** - -#### 4\. Community and Ecosystem - -The Next.js community (just like the JavaScript community in general) is large and quite active, with an ever-growing number of plugins, integrations, and third-party tools. The framework has solid documentation and an active ecosystem, thanks to its close ties to both the React community and Vercel’s developer support. - -⭐ **Rating: 5/5** - -#### 5\. Learning Curve - -For developers already familiar with React, Next.js provides a relatively smooth learning curve. However, for those new to SSR, SSG or even RSC (React Server Components), there’s a bit of a learning curve as you adapt to these concepts (after all, you’re learning React and backend development at the same time). That said, the framework's excellent documentation and active community make it easier to grasp. - -⭐ **Rating: 4/5** - -#### 6\. Security - -Next.js doesn’t inherently have a wide array of built-in security tools, but it follows secure default practices and can be paired with Vercel’s security optimizations for additional protection. Out of the box, Next.js ensures some level of security against common web threats but will need further configuration depending on the app's complexity. - -⭐ **Rating: 3.5/5** - -#### 7\. Future-Proofing - -Backed by Vercel, Next.js has a bright future. Vercel consistently pushes updates, introduces new features, and improves the overall developer experience. Given its adoption and strong support, Next.js is very future-proof, with no signs of slowing down. - -⭐ **Rating: 5/5** - -### Fastify - -![Fastify](https://assets.roadmap.sh/guest/logo-fastify-3bw4o.png) - -Fastify is a lightweight and fast backend framework for Node.js, often seen as a high-performance alternative to Express.js. It was created with a strong focus on speed, low overhead, and developer-friendly tooling, making it a popular choice for developers building APIs and microservices. Fastify offers a flexible plugin architecture and features like schema-based validation and HTTP/2 support, setting it apart in the Node.js ecosystem. - -#### 1\. Performance - -Fastify shines when it comes to performance. It’s optimized for handling large amounts of requests with low latency, making it one of the fastest Node.js frameworks available. - -⭐ **Rating: 5/5** - -#### 2\. Scalability - -With a strong focus on scalability, Fastify is ideal for handling large applications and high-traffic scenarios. Its lightweight nature ensures that you can build scalable services with minimal resource consumption. - -⭐ **Rating: 5/5** - -#### 3\. Flexibility - -Fastify’s [plugin architecture](https://fastify.dev/docs/latest/Reference/Plugins/) is one of its key strengths. This allows developers to easily extend the framework’s capabilities and tailor it to specific use cases, whether you’re building APIs, microservices, or even server-rendered applications. - -⭐ **Rating: 5/5** - -#### 4\. Community and Ecosystem - -While Fastify’s community is not as large as Express.js or Next.js, it is steadily growing. The ecosystem of plugins and tools continues to expand, making it easier to find the right tools and libraries to fit your needs. However, its smaller ecosystem means fewer third-party tools compared to some of the more established frameworks. - -⭐ **Rating: 3/5** - -#### 5\. Learning Curve - -If you’re coming from Express.js or another Node.js framework, Fastify’s learning curve is minimal. Its API is designed to be familiar and easy to adopt for Node.js developers. While there are some differences in how Fastify handles things like schema validation and plugins, it’s a relatively smooth transition for most developers. - -⭐ **Rating: 4/5** - -#### 6\. Security - -Fastify incorporates built-in security features such as schema-based validation, which helps prevent vulnerabilities like injection attacks. The framework also supports HTTP/2 out of the box, which provides enhanced security and performance. - -⭐ **Rating: 4/5** - -#### 7\. Future-Proofing - -Fastify has a strong development roadmap and is [consistently updated](https://github.com/fastify/fastify) with performance improvements and new features. The backing from a growing community and its continued adoption by large-scale applications make Fastify a solid bet for long-term use. - -⭐ **Rating: 5/5** - -### SvelteKit - -![SvelteKit](https://assets.roadmap.sh/guest/logo-sveltekit-9ntqz.png) - -SvelteKit is a full-stack framework built on top of Svelte, a front-end compiler that moves much of the heavy lifting to compile time rather than runtime. SvelteKit was designed to simplify building modern web applications by providing server-side rendering (SSR), static site generation (SSG), and support for client-side routing—all in a performance-optimized package. In other words, it’s an alternative to Next.js. - -#### 1\. Performance - -SvelteKit leverages Svelte compile-time optimizations, resulting in fast runtime performance. Unlike frameworks that rely heavily on virtual DOM diffing, Svelte compiles components to efficient JavaScript code, which means fewer resources are used during rendering. - -⭐ **Rating: 5/5** - -#### 2\. Scalability - -While SvelteKit is excellent for small to medium-sized applications, its scalability for enterprise-level applications is still being tested by the developer community. It is possible to scale SvelteKit for larger applications, especially with the right infrastructure and server setup, but it may not yet have the same level of proven scalability as more mature frameworks like Next.js or Fastify. - -⭐ **Rating: 4/5** - -#### 3\. Flexibility - -As with most web frameworks, SvelteKit is highly flexible, allowing developers to build everything from static sites to full-stack robust web applications. It provides SSR out of the box, making it easy to handle front-end and back-end logic in a single codebase. Additionally, it supports various deployment environments like serverless functions or traditional servers. - -⭐ **Rating: 5/5** - -#### 4\. Community and Ecosystem - -The SvelteKit community is growing rapidly, and more tools, plugins, and resources are being developed to support it. While the ecosystem isn’t as mature as frameworks like React or Vue, the rate of adoption is promising. The official documentation is well-written, and there’s a growing number of third-party tools, libraries, and guides available for developers to tap into. - -⭐ **Rating: 3.5/5** - -#### 5\. Learning Curve - -For developers familiar with Svelte, the transition to SvelteKit is smooth and intuitive. However, if you're new to Svelte, there is a moderate learning curve, particularly in understanding Svelte’s reactivity model and SvelteKit's routing and SSR features. Still, the simplicity of Svelte as a framework helps ease the learning process compared to more complex frameworks like React or Angular. - -⭐ **Rating: 4/5** - -#### 6\. Security - -SvelteKit’s security features are still evolving, with basic protections in place but requiring developers to implement best practices to build really secure web applications. There are no significant built-in security tools like in some larger frameworks, so developers need to be cautious and handle aspects like input validation, cross-site scripting (XSS) protection, and CSRF manually. - -⭐ **Rating: 3/5** - -#### 7\. Future-Proofing - -Svelte’s increasing popularity and SvelteKit’s rapid development signal a bright future for the framework. The growing adoption of Svelte, backed by its simplicity and performance, ensures that SvelteKit will continue to be developed and improved in the coming years. - -⭐ **Rating: 5/5** - -### Ruby on Rails - -![Ruby on Rails](https://assets.roadmap.sh/guest/logo-rails-eg1x0.png) - -Ruby on Rails (Rails) is a full-stack web development framework written in Ruby, created by David Heinemeier Hansson in 2004\. Rails revolutionized web development by promoting "convention over configuration" and allowing developers to rapidly build web applications with fewer lines of code. It - -#### 1\. Performance - -Rails performs exceptionally well for typical CRUD (Create, Read, Update, Delete) applications, where database operations are straightforward and heavily optimized within the framework. However, as applications grow in complexity or require real-time features, Rails’ performance can become a challenge. - -⭐ **Rating: 3.5/5** - -#### 2\. Scalability - -Rails is often critiqued for its scalability limitations, but it can scale when combined with proper architecture and best practices. Techniques like database sharding, horizontal scaling, and using background jobs for heavy-lifting tasks can help. Still, it’s not the first choice for developers who anticipate massive scale, as it requires careful planning and optimization to avoid performance bottlenecks. - -⭐ **Rating: 3.5/5** - -#### 3\. Flexibility - -Rails is a great framework for rapid development, especially for standard web applications, such as e-commerce platforms, blogs, or SaaS products. However, it’s less flexible when it comes to non-standard architectures or unique application needs. It’s designed with conventions in mind, so while those conventions help you move fast, they can become restrictive in more unconventional use cases. - -⭐ **Rating: 3.5/5** - -#### 4\. Community and Ecosystem - -Next to JavaScript with NPM, Rails has one of the most mature ecosystems in web development, with a huge repository of gems (libraries) that can help speed up development. From user authentication systems to payment gateways, there’s a gem for almost everything, saving developers from reinventing the wheel. The community is also very active, and there are many resources, tutorials, and tools to support developers at every level. - -⭐ **Rating: 5/5** - -#### 5\. Learning Curve - -Rails is known for its easy learning curve, especially for those new to web development. The framework’s focus on convention over configuration means that beginners don’t need to make many decisions and can get a functional app up and running quickly. On top of that, Ruby’s readable syntax also makes it approachable for new devs. - -However, as the application grows, mastering the framework’s more advanced concepts and learning how to break through those pre-defined conventions can become a bit of a problem. - -⭐ **Rating: 4/5** - -#### 6\. Security - -Rails comes with a solid set of built-in security features, including protections against SQL injection, XSS (cross-site scripting), and CSRF (cross-site request forgery). By following Rails' conventions, developers can implement secure practices without much additional work. However, as with any framework, you still need to stay updated on security vulnerabilities and ensure proper coding practices are followed. - -⭐ **Rating: 4.5/5** - -#### 7\. Future-Proofing - -While Rails is still highly relevant and widely used, its growth has slowed down from its initial hype during 2010, and it’s no longer the hot, new framework. That said, it remains a solid choice for many businesses, especially those building content-heavy or e-commerce applications. With an established user base and regular updates, Rails is not going anywhere, but its popularity may continue to wane as newer frameworks gain traction. - -⭐ **Rating: 4/5** - -### Laravel - -![Laravel](https://assets.roadmap.sh/guest/logo-laravel-iteyj.png) - -Laravel is a PHP backend framework that was introduced in 2011 by Taylor Otwell. It has since become one of the most popular frameworks in the PHP ecosystem, known for its elegant syntax, ease of use, and focus on developer experience (known to some as the RoR of PHP). Laravel offers a range of built-in tools and features like routing, authentication, and database management, making it ideal for building full-featured web applications quickly. - -#### 1\. Performance - -Laravel performs well for most typical web applications, especially CRUD operations. However, for larger, more complex applications, performance can be a concern. Using tools like caching, query optimization, and Laravel’s built-in optimization features (such as queue handling and task scheduling) can help boost performance, but some extra work may be required for high-load environments. - -⭐ **Rating: 4/5** - -#### 2\. Scalability - -Laravel can scale, but like Rails, it requires careful attention to architecture and infrastructure. By using horizontal scaling techniques, microservices, and services like AWS or Laravel’s Vapor platform, you can build scalable applications. However, Laravel is often seen as better suited for small to medium applications without heavy scaling needs right out of the box. - -⭐ **Rating: 3.5/5** - -#### 3\. Flexibility - -Laravel is highly flexible, allowing you to build a wide variety of web applications. With built-in features for routing, ORM, middleware, and templating, you can quickly build anything from small websites to enterprise applications. - -⭐ **Rating: 5/5** - -#### 4\. Community and Ecosystem - -Contrary to popular belief (mainly due to a lack of hype around the technology), Laravel has a large, active community and a vast ecosystem of packages and third-party tools. With Laracasts, a popular video tutorial platform, and Laravel.io the community portal for Laravel developers, there are many ways to reach out and learn from others. - -⭐ **Rating: 5/5** - -#### 5\. Learning Curve - -Laravel has a relatively gentle learning curve, especially for developers familiar with PHP. Its focus on simplicity, readable syntax, and built-in features make it easy to pick up for beginners. However, mastering the full list of Laravel’s capabilities and best practices can take some time for more complex projects. - -⭐ **Rating: 4.5/5** - -#### 6\. Security - -Just like others, Laravel comes with built-in security features, such as protection against common vulnerabilities like SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF). The framework adheres to best security practices, making it easier for developers to build secure applications without much extra effort. - -⭐ **Rating: 4.5/5** - -#### 7\. Future-proofing - -Laravel is still highly relevant and continues to grow in popularity (having [recently secured a very substantial](https://laravel-news.com/laravel-raises-57-million-series-a) amount of money). It has a regular release schedule and a strong commitment to maintaining backward compatibility. With its consistent updates, active community, and growing ecosystem, Laravel is a solid choice for long-term projects. - -⭐ **Rating: 4.5/5** - -### Phoenix - -![Phoenix](https://assets.roadmap.sh/guest/phoenix-logo-5g60a.png) - -#### **Overview and History** - -Phoenix is a backend framework written in Elixir, designed to create high-performance, scalable web applications. It leverages Elixir's concurrency and fault-tolerant nature (inherited from the Erlang ecosystem) to build real-time, distributed systems. - -#### 1\. Performance - -Phoenix is known for its outstanding performance, particularly in handling large numbers of simultaneous connections. Thanks to Elixir’s concurrency model and lightweight processes, Phoenix can serve thousands of requests with minimal resource consumption. Real-time applications benefit especially from Phoenix’s built-in WebSockets and its LiveView feature for updating UIs in real-time without the need for JavaScript-heavy frameworks. - -⭐ **Rating: 5/5** - -#### 2\. Scalability - -Scalability is one of Phoenix’s biggest features. Because it runs on the Erlang VM, which was designed for distributed, fault-tolerant systems, Phoenix can scale horizontally and vertically with ease. - -⭐ **Rating: 5/5** - -#### 3\. Flexibility - -Phoenix is highly flexible, supporting everything from traditional web applications to real-time applications like chat apps and live updates. Its integration with Elixir’s functional programming paradigm and the BEAM virtual machine allows developers to build fault-tolerant, systems. The flexibility extends to how you can structure applications, scale components, and handle real-time events seamlessly. - -⭐ **Rating: 5/5** - -#### 4\. Community and Ecosystem - -Phoenix has a growing and passionate community, but it’s still smaller compared to more established frameworks like Rails or Laravel. However, it benefits from Elixir’s ecosystem, including libraries for testing, real-time applications, and database management. The community is supportive, and the framework’s documentation is detailed and developer-friendly, making it easy to get started. - -⭐ **Rating: 2.5/5** - -#### 5\. Learning Curve - -Phoenix, being built on Elixir, has a steeper learning curve than frameworks based on more common languages like JavaScript or PHP. Elixir’s functional programming model, while powerful, can be challenging for developers unfamiliar with the paradigm. - -⭐ **Rating: 3.5/5** - -#### 6\. Security - -As with most of the popular backend frameworks, Phoenix comes with strong built-in security features, including protections against common vulnerabilities like XSS, SQL injection, and CSRF. Additionally, because Elixir processes are isolated, Phoenix applications are resilient to many types of attacks. While some manual work is still required to ensure security, Phoenix adheres to best practices and provides tools to help developers write secure code. - -⭐ **Rating: 4.5/5** - -#### 7\. Future-Proofing - -Phoenix has a bright future thanks to its solid foundation in the Erlang/Elixir ecosystem, which is known for long-term reliability and support. While the framework might be technologically sound and future-proof, the key to having Elixir in the future will depend on the growth of its popularity. If Elixir’s community keeps growing, we’ll be able to enjoy the framework for a long time. - -⭐ **Rating: 5/5** - -### Actix - -![Actix](https://assets.roadmap.sh/guest/logo-actix-rust-31pi3.png) - -Actix is a powerful, high-performance web framework written in Rust. It’s based on the actor model, which is ideal for building concurrent, distributed systems. Actix is known for its incredible performance and memory safety, thanks to Rust’s strict compile-time guarantees. - -#### 1\. Performance - -Actix is one of the fastest web frameworks available, thanks to Rust’s system-level performance and Actix’s use of asynchronous programming. As it happens with JavaScript-based frameworks, it can handle a large number of requests with minimal overhead, making it ideal for high-performance, real-time applications. - -⭐ **Rating: 5/5** - -#### 2\. Scalability - -The actor model makes Actix the best at handling concurrent tasks and scaling across multiple threads or servers. Rust’s memory safety model and Actix’s architecture make it highly efficient in resource usage, meaning applications can scale well without excessive overhead. - -⭐ **Rating: 5/5** - -#### 3\. Flexibility - -Actix is flexible but requires a deeper understanding of Rust’s ownership and concurrency model to fully take advantage of it. It’s great for building both small, fast APIs and large, service architectures. While Actix is powerful, it’s less forgiving compared to other popular backend frameworks like Node.js or Python’s Flask, where rapid prototyping is easier. - -⭐ **Rating: 3/5** - -#### 4\. Community and Ecosystem - -Rust’s ecosystem, while growing, is still smaller compared to more established languages like JavaScript or Python. However, the Rust community is highly engaged, and support is steadily improving. - -⭐ **Rating: 3.5/5** - -#### 5\. Learning Curve - -Actix inherits Rust’s learning curve, which can be steep for developers new to systems programming or Rust’s strict memory management rules. However, for developers already familiar with Rust, Actix can be a great gateway into web development. - -⭐ **Rating: 2/5** - -#### 6\. Security - -Rust is known for its memory safety and security guarantees, and Actix benefits from these inherent strengths. Rust’s compile-time checks prevent common security vulnerabilities like null pointer dereferencing, buffer overflows, and data races. While these features tackle one side of the security ecosystem, more relevant ones like web-related vulnerabilities are not tackled by the framework. - -⭐ **Rating: 2.5/5** - -#### **7\. Future-Proofing** - -Rust’s growing popularity and adoption, especially in performance-critical areas, ensure that Actix has a strong future. While Actix’s ecosystem is still developing, the framework is regularly maintained and benefits from Rust’s long-term stability. - -⭐ **Rating: 4.5/5** - -## Conclusion - -Choosing the right backend framework is a critical decision that can shape the future of your project. In 2024, developers have more powerful options than ever, from popular backend frameworks like Ruby on Rails, Laravel or Next.js to high-performance focused, like Fastify, SvelteKit, Phoenix, and Actix. Each framework has its own strengths, making it essential to consider factors such as performance, scalability, flexibility, and the learning curve to ensure you pick the right tool for the job. - -Ultimately, there’s no proverbial silver bullet that solves all your problems. Your choice will depend on your project’s needs, your team's expertise, and the long-term goals of your application. - -So take your time, weigh the pros and cons, and pick the framework that aligns best with your vision for the future. \ No newline at end of file diff --git a/src/data/guides/backend-job-description.md b/src/data/guides/backend-job-description.md deleted file mode 100644 index bafb00696..000000000 --- a/src/data/guides/backend-job-description.md +++ /dev/null @@ -1,274 +0,0 @@ ---- -title: "Backend Developer Job Description [@currentYear@ Template]" -description: 'Learn how to write the perfect backend developer job description and get my best tips on how to recruit backend dev talent effectively.' -authorId: ekene -excludedBySlug: '/backend/job-description' -seo: - title: "Backend Developer Job Description [@currentYear@ Template]" - description: '' - ogImageUrl: 'https://assets.roadmap.sh/guest/backend-job-description-nn3ja.png' -relatedTitle: "Other Guides" -relatedGuidesId: backend -isNew: false -type: 'textual' -date: 2024-11-12 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Backend developer job description example and template.](https://assets.roadmap.sh/guest/backend-job-description-nn3ja.png) - -Hiring the right candidate for a [backend engineering](https://roadmap.sh/backend) role requires identifying individuals who possess the necessary technical and soft skills and align with your organization's values. Selecting the right candidate can be challenging with a high volume of applications. - -Knowing what to prioritize when evaluating candidates is necessary for choosing the best fit for this role. In this guide, I’ll outline the key skills and qualities I focus on as a hiring manager when hiring a backend engineer. - -Here is a summary of the backend developer job description: - -- Design, develop, and maintain highly performant, reliable, scalable, and secure backend systems and server side logic. -- Oversee projects from conception to deployment, ensuring smooth execution and delivery to create a great and on-brand user experience. -- Maintain and optimize the backend infrastructure to meet evolving project needs. -- Collaborate with front-end developers to integrate user-facing elements with server-side logic. -- Write clean, standardized, maintainable, testable, and reusable backend code to solve business and technical problems. - -Let’s look at the backend engineer role in more detail. - -## Backend engineer job description template - -The job description of a backend developer differs depending on the team, project, or organization. Based on my experience as a backend engineer recruiter, here is a sample job description template that outlines the key skills and qualifications hiring managers look for in potential candidates: - -**Job title: Backend Engineer** - -**Company:** [Company Name] - -**Location:** [Location, region, hybrid, or remote] - -**Job Type:** [Full-time, Part-time, or Contract]. - -**About us** -[Give a brief introduction of your company and what you do]. - -**Job Description** -**[Company Name]** is looking for experienced backend engineers who are passionate about building scalable, efficient, and reliable server-side applications. This role requires strong proficiency in server-side languages (such as Python, Java, or Node.js), databases (SQL, NoSQL), and API development. The ideal candidate will have a solid understanding of backend architecture, security best practices, and cloud technologies to support modern applications. - -Collaboration with frontend developers, designers, product managers, and other teams is key to this role, so strong communication skills are important. We are looking for strong problem solvers who can apply their engineering skills across different platforms and environments to deliver the best possible experience for customers and internal users. - -**Responsibilities** - -- Take ownership of the design, architecture, development, deployment and operations of microservices you will develop, using DevOps practices, pair programming and other cutting-edge methodologies. -- Promote a highly collaborative environment with other product team members. -- Participate in regular technical forums to share knowledge with other engineers. -- Be an active member of cross-functional agile teams collaborating with product owners, frontend developers, designers, and business intelligence teams. -- Build the server side logic of our web application. - -**Requirements** - -- Solid backend experience with microservice and distributed event-driven architectural patterns -- Degree in computer science or any related discipline. -- Professional experience with one backend programming language, e.g., Python, C#, Java -- Experience working with docker and containerization technologies such as Kubernetes. -- A deep understanding of continuous integration and continuous delivery. -- Practical experience in applying advanced design concepts such as Domain Driven Design (DDD), Object Oriented Programming (OOP). -- Strong communication and collaboration skills. - -**What we offer** -[Itemize the different perks you offer, for example, training allowance, competitive salary, home office setup, etc.]. - -**How to apply** -If this role excites you, please submit your resume and cover letter to **[contact email or link to** **job** **portal]**. - -You have seen what a sample backend engineer job advert looks like; now, let’s have a detailed look at a backend developer’s responsibilities. - -## Key backend developer responsibilities - -The roles and responsibilities of a backend engineer could vary depending on the project requirements, company size, or team structure. However, there are typical roles and responsibilities that cut across board. They include: - -- Server-side development -- Application Programming Interface (API) development -- Database administration and management -- Performance optimization -- Integration of third-party services -- Testing -- Documentation -- Collaboration - -![Back-end engineer responsibilities](https://assets.roadmap.sh/guest/backend-engineer-responsibilities.jpg) - -Typical projects that backend developers work on include: - -- RESTful APIs that perform CRUD operations. -- Building web scraping and data processing services. -- Building image processing services. -- Designing and implementing content delivery networks (CDNs). -- Modeling and designing database systems. - -Let’s look at their individual responsibilities in detail: - -### Server-side development - -This is one of the core responsibilities of a backend developer. It involves writing server-side web application logic to handle requests from the frontend, communicate with database systems, and handle an application’s business and backend logic. - -### API development - -A backend developer designs, implements, and maintains APIs for communicating between different services or parts of an application and with external systems. This involves creating endpoints, responding to requests, and ensuring API security. [Swagger](https://swagger.io/) is a standard tool used by backend engineers to design and document APIs. - -### Database administration and management - -Data plays a central role in software and application development. A backend developer decides how and where to store these data. Databases are one of the most used data storage solutions, and a backend developer designs and maintains them for efficient data management. The tasks here include deciding which databases to use, either [SQL](https://roadmap.sh/sql) or NoSQL databases, also known as document databases, choosing the right Object Relation Mapper (ORM) for mapping objects to a database, writing efficient queries, optimizing database performance, and ensuring data security. - -### Performance optimization - -Backend engineers continually look for ways to optimize performance. They identify performance bottlenecks such as slow database queries, poor code quality, and high application latency and work towards resolving them. This involves refactoring to ensure high-quality reusable code, updating dependencies, redesigning the database schema, etc. - -### Integration of third-party services - -Some applications integrate with third-party web services or APIs to perform specific tasks. Some of these third-party services include payment gateways, cloud services such as Google Cloud and [Amazon Web Services](https://roadmap.sh/aws), and user authentication services, such as Auth0. Backend engineers integrate these external services into an application. - -### Testing and bug fixing - -Backend engineers write unit, integration, and end-to-end tests to ensure system reliability. These tests help to keep the system *up* and bug-free during continuous development. Troubleshooting and fixing are also part of a backend engineer’s primary responsibility. - -### Documentation - -A backend developer writes and maintains technical specifications, API documentation, and guides for existing and new team members. These documents are forms of knowledge transfer and can be referenced when needed. - -### Collaboration - -Backend engineers collaborate with other team members, including frontend developers, UI/UX designers, project managers, product managers, etc., to achieve a common goal. - -## Skills and qualifications needed to excel as a backend engineer - -A backend engineer needs a combination of soft and technical skills to excel. Some of the skills you should look out for when hiring a backend engineer include: - -- Knowledge of at least one backend programming language -- In-depth understanding of databases and caching -- Knowledge of Application Programming Interfaces (APIs) -- Basic knowledge of web servers -- Knowledge of design patterns -- Familiarity with version control systems -- Understanding of security best practices -- Collaboration and communication skills - -### Knowledge of at least one backend programming language - -A [backend developer](https://roadmap.sh/backend/developer-skills) should have an in-depth knowledge of at least one backend programming language, such as [Java](https://roadmap.sh/java), C#, [Python](https://roadmap.sh/python), [Node.js](https://roadmap.sh/nodejs), etc. It is also beneficial for a backend engineer to be familiar with some [backend frameworks](https://roadmap.sh/backend/frameworks) such as Django, [ASP.NET](https://roadmap.sh/aspnet-core), Ruby on Rails, [Sprint Boot](https://roadmap.sh/spring-boot), etc. - -![Backend languages](https://assets.roadmap.sh/guest/backend-languages.png) - -### In-depth understanding of databases and caching - -A backend developer should know how databases and caching work in robust web applications. Many types of databases are used in backend systems, including relational and document databases. Examples of Database Management Systems used in backend systems include Microsoft SQL Server, PostgreSQL, MySQL, MongoDB, etc. - -[Caching](https://roadmap.sh/guides/http-caching) is a process of temporarily storing data that is requested regularly. It helps avoid recurrent unnecessary database queries. Redis is an example of an in-memory data store used for caching. - -### Knowledge of APIs - -APIs are used to communicate between different systems. These systems could be microservices, frontend and backend systems, or third-party systems. A backend developer is expected to know how to [design APIs](https://roadmap.sh/api-design) and make them available to consumers. - -### Basic knowledge of web servers - -A backend engineer should have a basic understanding of web server technologies. Web servers respond to client requests. An example of a web server is Nginx. During the interview process, you should ensure that the candidate understands how web servers work and also know how to configure them. - -### Knowledge of design patterns - -[Design patterns](https://roadmap.sh/guides/design-patterns-for-humans) are reusable code libraries or solutions to common problems in designing and structuring software components. A backend developer should know some of these design patterns. The common types of design patterns are: - -- Creational patterns -- Structural patterns -- Behavioral patterns -- Architectural patterns - -### Familiarity with version control systems - -Version control systems help track and maintain a history of code changes made to an application. With a proper version control system, multiple developers can work on a codebase simultaneously. Through version control, they collaborate on a code repository. - -A backend engineer is expected to have proficient knowledge of version control. Git is one of the most common version control systems today. There are many code repositories, including Bitbucket, GitHub, and GitLab. - -### Understanding of security best practices - -A backend developer should have a basic understanding of standard security practices and implement robust security measures to protect sensitive data and prevent unauthorized access to data. Common vulnerabilities include SQL injection, cross-site scripting, and cross-site request forgery. About every large cloud provider has features that provide security measures, including AWS IAM, Azure Active Directory, and Google Cloud Identity. - -### Collaboration and communication skills - -A backend developer typically collaborates with other engineers, managers, and testers across multiple teams to ensure consistency and improve user experience. During the interview process, you should pay close attention to the candidate's communication skills because a backend developer should be able to clearly communicate problems and their approach to a solution. Ask the candidate questions that border on collaboration and see how they respond. - -## Additional skills that will make a candidate stand out as a backend developer - -The role of a backend developer is very competitive. However, there are some skills that a backend developer should possess to make them stand out. Some of these skills include: - -- Continuous learning -- Problem-solving skills -- Time management skills -- DevOps skills - -### Continuous learning - -The field of [backend engineering](https://roadmap.sh/backend) is continually expanding, so be sure to look out for candidates who enjoy learning. Find out if they stay up to date with the latest technologies and industry trends and how often they try out new things. In addition to acquiring new skills, you should also find out if they are willing to share knowledge and help in team development. - -### Problem-solving skills - -While it’s essential to use problem-solving tools, intrinsic problem-solving skills are equally important for a backend developer. Your potential hire should have an analytical mindset and be able to break complex problems into smaller chunks that can be solved incrementally. - -### Time management skills - -Engineers sometimes have chaotic workloads, which often lead to burnout and affect their performance. During the interview, ask the candidate about their time management practices and how they manage heavy workloads. Effective time management and prioritizing can make a candidate stand out as an engineer and avoid burnout. - -### DevOps skills - -Knowledge of [docker](https://roadmap.sh/docker) and container orchestration technologies, such as [Kubernetes](https://roadmap.sh/kubernetes) is another skill that makes a backend developer stand out. A firm understanding of continuous integration and continuous deployment (CI/CD) setups is a plus. Consider a candidate with this basic DevOps skill for the role. - -## Common backend engineer job interview questions - -This curated list of the common backend developer job interview questions should help your search: - -**What are the different HTTP methods?** -Here, the candidate should be able to explain the different HTTP methods, which are GET, POST, PUT, and DELETE. - -**Describe what you understand** **about continuous integration and continuous delivery** -The candidate is expected to explain what CI/CD means in clear terms. - -**What are databases****,** **and what are the different types of databases?** -The candidate should know what a database is and what relational and document databases are, along with examples. - -**What is object****-****oriented programming?** -The candidate should be able to explain OOP, a process of designing software around objects. - -**Explain the difference between synchronous and asynchronous programming** -Here, you should look for the candidate’s understanding of sync and async processes. Synchronous programming executes tasks sequentially, and each task must be completed before the next start. Asynchronous programming allows tasks to run independently without waiting for each other to complete. It uses promises or async/await to handle task completion. - -**What is the purpose of caching in a backend system****,** **and what caching strategies have you used?** -The candidate should be able to explain what caching means and also the different caching strategies, which include: - -- Full-page caching -- Data caching -- Result caching - -**What are some common security vulnerabilities in web applications****,** **and how will you mitigate them?** -The candidate should be able to explain common security vulnerabilities, such as cross-site scripting, SQL injection, cross-site request forgery (CSRF), and so on. You can mitigate them by performing proper input validation and secure session management. - -## Effective interview strategies and assessments - -Evaluating backend engineers requires targeted assessments that reveal technical skills and problem-solving abilities. Here are practical techniques to help you identify qualified candidates: - -- **Code review exercises:** Provide a sample code snippet on any backend programming languages with issues or inefficiencies. Ask candidates to identify problems, propose improvements, and explain their reasoning. This exercise tests attention to detail and understanding of best practices. -- **Live coding tasks:** Use short coding challenges focused on common backend development tasks like database queries or API calls. Live coding reveals how candidates approach problems under pressure and handle real-time debugging. -- **System design discussions:** Present a simple system design prompt, like designing a scalable file storage system. Listen for clarity in explaining their thought process, handling of trade-offs, and understanding of backend fundamentals. -- **Behavioral questions on past projects:** Ask about specific backend challenges they’ve faced and how they solved them. Look for evidence of adaptability, collaboration, and knowledge depth, such as experiences with database optimization or API integration. -- **Assess communication skills:** Observe how they explain technical concepts. Strong candidates communicate clearly and adapt explanations for both technical and non-technical audiences. - -These methods offer a well-rounded view of a candidate’s backend development skills and experience. - -## What’s next? - -Backend engineers play a vital role in software engineering, and great ones are in high demand. Now that you have a clear understanding of what to look for, it’s time to build a hiring process that identifies candidates with the right technical skills and collaborative mindset. - -The foundation of your tech stack depends on a strong backend. You can find potential hires in the roadmap.sh [community](https://roadmap.sh/discord) of beginner and experienced backend developers. - -roadmap.sh also offers valuable resources to help you and your team stay ahead by helping you to: - -- Create a personalized or [team-based roadmap](https://roadmap.sh/teams). -- Become part of a supportive community by [signing up on](https://roadmap.sh/signup) [roadmap.sh](http://roadmap.sh) [platform](https://roadmap.sh/signup). diff --git a/src/data/guides/backend-languages.md b/src/data/guides/backend-languages.md deleted file mode 100644 index 65f6b53c6..000000000 --- a/src/data/guides/backend-languages.md +++ /dev/null @@ -1,384 +0,0 @@ ---- -title: 'The 5 Best Backend Development Languages to Master (@currentYear@)' -description: 'Discover the best backend development languages to master in @currentYear@.' -authorId: fernando -excludedBySlug: '/backend/languages' -seo: - title: 'The 5 Best Backend Development Languages to Master (@currentYear@)' - description: 'Discover the best backend development languages to learn right now for career development, with practical tips from an experienced developer.' - ogImageUrl: 'https://assets.roadmap.sh/guest/backend-languages-2x930.jpg' -relatedTitle: "Other Guides" -relatedGuidesId: backend -isNew: false -type: 'textual' -date: 2024-01-18 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![The best backend languages to learn.](https://assets.roadmap.sh/guest/backend-languages-2x930.jpg) - -Web development is typically divided into two main categories: [backend development](https://roadmap.sh/backend) and [frontend development](https://roadmap.sh/frontend). Frontend development focuses on the visible part of the application for end-users, i.e. the part that users interact with, while backend development involves writing code that remains unseen but is essential for the functioning of all applications—the business logic. - -Two of the key components when it comes to backend development are the programming language that you are going to code in and the database. In this article, we will be looking into a subset of programming languages that could be used for backend development. We will be discussing the pros and cons of each and the community to help you pick the best programming language for backend development. - -Diving straight in, I will cover the top 5 backend programming languages that you should to learn if you’re looking to broaden your horizons in the server-side world. - -## Top 5 Backend Languages to Learn for Web Development - -The best backend languages to learn in 2025 are: - -- Python -- Java -- JavaScript -- PHP -- Go - -Why these languages specifically? - -They’re some of the most known and used languages in the industry right now (see [StackOverflow’s 2023 Developer Survey](https://survey.stackoverflow.co/2023/#most-popular-technologies-language-prof)). That said, keep in mind that these are all great options, and they’re not presented here in any particular order. - -### Python - -[Python](https://roadmap.sh/python) has been around for decades already and while it’s never been the most popular option, it has always managed to stay within the top 5 choices. People love it for being easy to read, straightforward, and able to handle all sorts of tasks, making it a top pick for developers globally. Sure, Python's got a big name in data processing and machine learning, but let's not forget its solid impact on web development!. - -What makes Python extra appealing, especially for beginners, is the fact that reading and writing it feels very much like English (or at least, as pseudo code). This makes it a top choice for folks just starting out in coding. - -#### Beginner Accessibility and Learning Resources - -One of Python's standout features is its beginner-friendly syntax, making it an ideal language for those new to programming. The emphasis on readability and the absence of complex syntax (for the most part), eases the learning curve, enabling new developers to quickly grasp fundamental concepts. - -Python's community plays a critical role in its accessibility. Abundant learning resources, tutorials, and documentation are readily available, empowering beginners to progress from basic programming principles to advanced backend development seamlessly. Online platforms like Codecademy, Coursera, Real Python, and even Google offer comprehensive courses tailored to all skill levels. - -#### Practical Applications and Popular Frameworks - -Python's versatility is evident in its applicability across a spectrum of industries, from web development and data science to artificial intelligence and automation. In the context of backend development, Python shines brightly with its two standout [backend frameworks](https://roadmap.sh/backend/frameworks): [Django](https://www.djangoproject.com/) and [Flask](https://github.com/pallets/flask). - -##### Django - -So, let's talk about Django – it's like the superhero of web frameworks, at least in the world of Python. This high-level powerhouse is all about that "batteries-included" style, giving you a whole package of tools and features that make development lightning-fast. It follows the [Model-View-Controller](https://www.crio.do/blog/understand-mvc-architecture/) (MVC) architecture that gives your web applications a solid structure, making them scalable and easy to keep up. - -Part of that whole “batteries-included” motto means that it comes with an admin panel, an ORM (Object-Relational Mapping) for smooth data handling, and security features that make sure your project is secured out-of-the-box. All these goodies work together, making Django a top choice for projects, whether they're a walk in the park or a brain-bending challenge. - -##### Flask - -On the other hand, Flask, a micro-framework, takes a minimalist approach, providing developers with the flexibility to choose components as needed. While Flask may be lighter than Django, it doesn't compromise on functionality. Its simplicity and modularity make it an excellent choice for small to medium-sized projects, allowing developers to tailor the framework to meet specific project requirements. - -Both Django and Flask underscore Python's suitability for backend development, offering developers frameworks that cater to diverse project needs while maintaining Python's hallmark readability and efficiency. - -#### Pros and Cons of Python - -As with all other backend languages in this article, Python has some pros and cons you should consider before picking it as your first backend language. - -##### Pros - -- The syntax is very easy to learn because it’s very much like writing English. -- The ecosystem is quite mature and has some nice frameworks that will contain all the tools you need to get started - -##### Cons - -- It’s not the most popular backend language, so while the frameworks available are constantly updated, they aren’t necessarily using the latest technology trends. -- The GIL (Global Interpreter Lock) limits Python’s performance in multi-threaded applications. - -### Java - -[Java](https://roadmap.sh/java) has a massive presence and for good reason (according to [JetBrain’s survey in 2022, Java was used by 48% of developers](https://w3techs.com/technologies/details/pl-php)).It's the kind of programming language that's everywhere – serving up websites, running your favorite apps, and even powering big-scale enterprise systems. - -#### Is it worth learning Java? - -Now, learning Java, (a strongly typed, object oriented programming language (OOP)), is a journey worth taking, but it's not a walk in the park. It's a bit like climbing a mountain – you start at the bottom with the basics, and as you ascend, you get into the nitty-gritty of things like object-oriented programming. The process will force you to learn a lot, which is a great thing, by the end you’ll have a lot of understanding of mechanics and concepts around OOP that can be extrapolated into other languages. However, that can also be overwhelming to some developers who just want to learn by building mini-projects. In those situations, the learning curve of Java might be too long (not steep, but long because there is a lot more to cover than with alternatives such as Python or JavaScript). - -That said, the community is big and there are tons of resources, from online courses to forums, helping you navigate the Java landscape. And good reason, considering Java has been around for quite a while. - -#### Use Cases and Robust Ecosystem - -Java's everywhere. From web development to mobile apps, and even diving into the world of big data, Java's got its fingerprints all over. And if it’s not the language itself, it’s the Java Virtual Machine (JVM) powering some other language, like Spark. - -Java’s the language of choice for many big enterprises given its innate robustness (the first version of the language was released in 1995, it’s had quite a long time to evolve), and its robust ecosystem of libraries and frameworks makes it a go-to for developers. - -#### Pros and Cons of Java - -For all its power and robustness, there are some negative (and positive) aspects to picking Java as your first backend language. - -##### Pros - -- Java has a mature ecosystem with a varied array of libraries and frameworks for you to try. The community has been working on them and evolving them for years in many cases, so they’re quite ready to develop enterprise-ready solutions. -- Java’s multithreading support makes it ideal for some processing-heavy tasks in the backend of big applications. -- Java’s heavy focus on object oriented programming makes it a great option for developers who enjoy that paradigm. - -##### Cons - -- Java’s verbose syntax might be a problem for some people. While you can still code without an issue, it all comes down to personal preference. If you like to write less and do more with your code, Java might not be the ideal pick for you. It’s verbosity can increase development time in some situations. -- Java applications can have higher memory consumption than others, especially compared to others like PHP. While the reason for this is their entire architecture, the fact remains. - -### JavaScript (Node.js) - -With Node.js in the mix, JavaScript becomes a lingua franca in web development. In other words, you can use the same language both for the frontend (client side) and for the backend (server side) of your application. - -That said, keep in mind that depending on the framework you’re using, while it might be JavaScript on both sides, the code and logic you use can be considerably different. - -Remember that frontend code is often confused with framework code (as in React, Angular, Vue, etc) by some developers, simply because it’s all JavaScript. But don’t be confused, the backend lacks a lot of the extra “juice” added by the browser (like the DOM API and others). - -#### Learning Advantages of Node.js - -If you've got the basics of JavaScript down, even if your experience has only been on the frontend, diving into Node.js is like leveling up. It lets you use the same language for both frontend and backend, which means less time juggling languages and more time building cool stuff. - -The event-driven, non-blocking architecture is one of the main features that make the language so special – it makes your web apps fast and efficient without you having to learn more complex concepts such as multi-threading. Plus, the community is constantly growing and there are tutorials everywhere to guide you through the Node.js universe. - -If you were to rank languages based on the amount of content out there to learn them, JavaScript would be one of the first ones (if not the first one) on the list. - -#### Key Frameworks and Development Tools - -Now, let's talk about frameworks. In the case of JavaScript, this topic is so varied that recommending a single option for someone just getting started is really hard. - -For example, if you want to go frontend agnostic, or in other words, you don’t care about the technology being used to develop the client side of your app, then a good starting option would be [Express.js](https://expressjs.com/). This framework used to be the industry standard. And while that’s no longer the case, it’s still a perfect first choice if you’re looking for something with the required functionality to make your life a lot easier. - -Now, if on the other hand, you’re looking to build the frontend and the backend at the same time, then I would recommend going with [Next.js](https://nextjs.org/) if you’re already familiar with React. Or if on the other hand, you prefer Vue.js, then definitely try [Nuxt](https://nuxt.com/). Either one of those will help you get the job done with all the bells and whistles you can think of. - -#### Does it make sense to pick up JavaScript as a backend language? - -The answer to this question is always going to be “yes”, whether you’re coming from the frontend and you already have JS experience or if you’re picking it up from scratch. In fact, according to [StackOverflow’s 2023 survey, JavaScript is the most used language by professionals](https://survey.stackoverflow.co/2023/#most-popular-technologies-language-prof) (with 65.85% of the votes). - -![JavaScript Interest](/guides/backend-languages/javascript-interest.png) - -If you’re coming from the client side, then adopting JS for your backend will let you start working in minutes. You’ll probably spend more time learning the backend-specific concepts than the language itself. - -On the other hand, if you’re coming from zero, or from other programming languages, JS has some quirks, for sure, but if you go the JS route, you’re already making way for a potential switch into the frontend in the future. Who knows, maybe in a few months you’ll also want to start working on the frontend, and by having picked up JS as your main backend language, you have 90% of the work already cut for you. - -There is really no downside to picking JS as your first backend language. - -#### Pros and Cons of JavaScript - -While there might not be a downside to picking JS, there is no perfect language out there, so let’s take a look at some of the pros and cons before moving on to the next one. - -##### Pros - -- Going with JavaScript, you’re using the same language on the backend and on the frontend. There is less cognitive load while switching environments if you’re the one coding both sides of the app. -- The ecosystem around JavaScript is one of the richest ones you can find. The community is constantly pushing the limits of the language and coming up with new solutions to everyday problems. -- The simple syntax allows you to reduce development time because you don’t have to write as much code to achieve good results. - -##### Cons - -- Asynchronous programming can be hard for some developers coming from other languages, like Python for example. -- The lack of strong types in JavaScript can cause some problems for big codebases. -- The single-thread nature of the language makes it really hard to implement CPU-intensive tasks. While there is support for multi-threading, it’s not extensive nor commonly used. -- Debugging asynchronous code can be difficult for new developers given the non-linear nature of it. - -### PHP - -Now, if you’re looking for something very well established in the web development industry, just like Java but with a shorter learning curve, then you’re probably looking for PHP. - -> As a note about PHP’s relevancy, while many developers might claim that PHP is a dying tech, according to [W3Techs, over 75% of websites with a backend use PHP](https://w3techs.com/technologies/details/pl-php). - -It's the glue that holds a ton of websites together, and its longevity in the web development scene is no accident. - -#### Ease of Mastery and Vast Library Support - -If you're diving into PHP, you wouldn’t be so wrong (no matter what others might tell you). It's got a gentle learning curve, which means you can start building things pretty quickly. Getting everything set up and working will probably take you 10 minutes, and you’ll be writing your first “hello world” 5 minutes after that. - -The vast community support and an ocean of online resources make mastering PHP a breeze. Plus, its library support is like having a toolkit that's always expanding – you'll find what you need, whether you're wrangling databases, handling forms, or making your website dance with dynamic content. - -If you’re looking to pick up PHP, look for the LAMP stack, which stands for **L**inux, **A**pache, **M**ySQL, and **P**HP. With that tech stack, you’ll have everything you need to start creating websites in no time. - -#### Modern PHP Frameworks and Their Impact - -If we’re talking about PHP frameworks, then we gotta talk about [Laravel](https://laravel.com/) and [Symfony](https://symfony.com/). They are like the rockstars of the modern PHP world. - -Laravel comes with a lot of tools and features that help you speed up your development process. On the other side, Symfony has a modular architecture, making it a solid choice for projects of all sizes. - -These frameworks showcase how PHP has evolved, staying relevant and powerful in the ever-changing landscape of web development. - -#### Pros and Cons of PHP - -Let’s take a look at some of the most common advantages of going with PHP for the backend and some cons to discuss why it might not be the best choice for you. - -##### Pros - -- PHP is designed for web development, which still makes it a very popular choice for a backend language. -- PHP’s community is quite big, considering how old the language is, so if you need help, chances are, someone has the answer you’re looking for. - -##### Cons - -- One of the major complaints developers have about the language is its inconsistent function naming convention. While not a huge problem, it makes it very hard for developers to intuitively find the right function by name. This causes you to constantly verify your code against the documentation to make sure you’re not making mistakes. - -### Go - -Now, let's close in on Go, the programming language born at Google that's all about simplicity and efficiency. Go embraces a clean and straightforward syntax. Despite its simplicity, it focuses heavily on performance, making it an excellent choice for building modern, scalable applications. - -According to the [PYPL index](https://pypl.github.io/PYPL.html) (using Google searches to weigh interest of developers in a particular language), we can see a clear worldwide growing interest in Go from the development community: - -![Go Interest](/guides/backend-languages/pypl-go-index.png) - -#### Concurrency and Scalability - -Go stands out in the crowd, especially when it comes to handling concurrency and scalability. Its built-in support for concurrent programming, through goroutines and channels, makes it a standout choice for applications that need to juggle multiple tasks simultaneously. This makes Go particularly adept at handling the demands of today's highly concurrent and distributed systems. In other words, Go is a great choice for building microservices, a type of system that is very commonly used as the backend for complex web applications. - -So yes, very relevant. - -#### Learning Curve and Developer Productivity - -Learning Go is a smooth ride, thanks to its simplicity and extensive documentation. Developers often find themselves quickly transitioning from understanding the basics to building robust applications. - -The language's focus on developer productivity is evident in its quick compilation times and the absence of excessive boilerplate code, allowing developers to concentrate on building features rather than wrestling with the language itself. - -#### Pros and Cons of Go - -Let’s take a look at some pros and cons for the last programming language on our list. - -##### Pros - -- Go code compiles quickly, which in turn leads to very fast development cycles. This is a big plus if you’re developing a big application with a large codebase because other options might slow down your process with their compilation times. -- Go's syntax is simple enough to make it easy to learn and understand for new developers. - -##### Cons - -- Go’s ecosystem is quite young when compared to the other alternatives here, so the maturity of the tools available might not be the same as, for example, Java or JavaScript tooling. - -## Choosing the Ideal Backend Language - -So, are these the best backend programming languages out there? Is there an absolute “best” backend programming language? - -As you’ve probably seen by now, there is no “perfect” or “ideal” backend language. When it comes to picking one out of the huge number of alternatives, you have to consider other aspects outside of the language itself: - -- What’s the size of your project? Are you building a large-scale platform? Or just a basic static website? -- Do you mind spending more time learning or do you need to be efficient and start coding right away? -- Do you already have some programming knowledge and would like to pick something that resembles what you already know? Or would you like to pick up something that’s entirely different? -- Are you alone? Or are you part of a team? - -Once you’ve answered those questions, you’ll probably have some idea of where to go, but then you should look into the language itself, specifically into: - -- Does it have enough learning resources for you? -- How big and active is the community around it? -- Are the main frameworks still under development? Or have they been parked for a while? - -In the end, you’re evaluating the language and its ecosystem, making sure they’re both evolving right along the web industry. If you find that there are aspects that are falling behind, then it probably isn’t a good choice. - -[![Roadmap.sh](/guides/backend-languages/backend-roadmap-part.png)](https://roadmap.sh/backend) - -

Small section of the full backend roadmap available on roadmap.sh

- -There you’ll find community-maintained roadmaps for many career paths within software development. In particular, for this article, the [backend roadmap](https://roadmap.sh/backend) is a great place to start, because while picking a backend language is important, you’ll see there that it’s not just about the language. In fact, there is a lot of tech around the language that is also required (I’m referring to databases, git, understanding how client-server communication works, and a big “etc). - -## What are backend languages? - -When it comes to web development, there is a very clear distinction between frontend and backend technologies. - -While the frontend ecosystem is quite limited to JavaScript (and other JavaScript-based languages, like TypeScript) due to Browser compatibility, the backend (A.K.A server-side) is a very different scenario. - -![Backend Languages](/guides/backend-languages/back-vs-front.png) - -Please note that neither of those lists is extensive, as they’re both constantly growing. - -You can think of a backend language as one that “runs on the server side”. That said, by that definition, any language is a backend language because even JavaScript nowadays can be used on the backend as well (thanks to Node, Bun, and Deno). - -However, we can go one step further and say: - -> “A backend language is a server side programming language that has the tools and frameworks required to build web backends” - -That will narrow down the list a little bit. But essentially, we can think of backend languages as anything that fits the following list: - -- Is able to listen for incoming HTTP/S connections. -- Can access databases. -- Can send HTTP/S requests. -- Can access the filesystem. -- Has a rich ecosystem of tools and frameworks to build web applications. - -With those features and a rich ecosystem of libraries and frameworks, a language can definitely be considered “backend-ready”. - -## Why learn a backend programming language? - -If you’re already a backend developer, then the main reason would be to become a more versatile developer. The more languages you know, the better code you’ll write. Either because you’ve learned different ways of solving the same problem, or simply because you’ll be able to contribute to more projects. - -If, on the other hand, you’re just getting started, the main reason would be that you’re entering an industry that’s always looking for people. - -## Is the market in demand for backend developers? - -One thing to keep in mind right now is that the software development market is always in demand for devs. For example, the [U.S. Bureau of Labor Statistics gives our industry a 25% growth rate](https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm) (demand-wise) from 2022 to 2023, whereas the mean for other industries is 3%. - -According to [JetBrains, during 2022, 75% of developers were involved with web development](https://www.jetbrains.com/lp/devecosystem-2022/) in one fashion or another, so whether you’re backend devs or frontend devs, there is always someone looking for you. - -That said, we also have to consider that this industry is becoming a global industry. While some countries might still be fighting that process back, most developers can directly access the global market and work for any company in the world. - -That is a great opportunity for a backend developer who lives in an area where there isn’t a big IT industry; however, it also means competing with every other developer in the world. That might cause the illusion that the IT industry is in recession or that there are no job opportunities for developers. - -There are always opportunities; it’s just that you're now after the same job that many others from around the world are after as well. - -That said, backend devs are always in high demand because they’re the bread and butter of any web application. They’re always required to create the underlying platform that will power most systems, so the key to standing out here is to stay up-to-date with the industry’s trends and technologies. - -Don’t get me wrong. You can’t possibly be expected to keep up with every single programming language that is released or with all the new frameworks that are created every month. However, keeping an eye on the industry and understanding what survives long enough to become a new industry standard is important. You have to keep updating yourself and learning new technologies/skills constantly; that way, you’ll stay relevant. - -## Is learning a backend language good for your career? - -Whether you’re a frontend developer looking to become full-stack (someone who can code both the backend and the frontend of an application), or if you’re new to the industry and you’re thinking if this might be the best place for you, the answer is “yes”. - -Backend developers are always in demand, so adding a new tool to your already existing toolbelt (if you’re already a dev) will only make you more flexible in the eyes of your employer. - -But remember, a language is just a language until you give it context. If you’re wondering whether or not one of the backend languages in this article might be for you, the answer will always be “yes”. However, understand that if backend development is new to you, you will also need to pick up other skills and backend technologies around the language, such as: - -- Understanding HTTP. -- Learning Structured Query Language (SQL) to query your databases. -- Understanding how APIs work. -- What server side rendering means and how to take advantage of it. -- What web servers are and how to use them/build them. - -Otherwise, you’ll understand the language, but you won’t know how to use it for the role of backend development. - -## Jumpstarting Your Backend Development Journey - -To get started with your backend development journey, it's crucial to have a roadmap that guides you through the learning process and equips you with the skills to build robust and scalable backend systems. - -Lucky for you, if you’re reading this, that means you’ve found the most complete and comprehensive roadmap online: [roadmap.sh](https://roadmap.sh), the current [backend roadmap](https://roadmap.sh/backend) is filled with details of everything you should and could (optionally) learn in your journey to becoming a backend developer. - -## Guided Learning: From Online Courses to Bootcamps - -Online courses and bootcamps serve as invaluable companions on your learning expedition. Platforms like Udemy, Coursera, and freeCodeCamp offer comprehensive backend development courses. - -These resources not only cover programming languages like Python, Java, or JavaScript but also dive deep into frameworks like Django, Express.js, or Laravel. For those seeking a more immersive experience, coding bootcamps provide intensive, hands-on training to fast-track your backend development skills. - -Whatever choice you go for, make sure you’re not following trends or just copying the learning methods of others. Learning is a very personal experience and what works for others might not work for you, and vice versa. So make sure to do the proper research and figure out what option works best for you. - -## Building Community Connections for Learning Support - -Joining developer communities (there are several on Twitter for example), forums like Stack Overflow, or participating in social media groups dedicated to backend development creates a network of support. - -Engaging with experienced developers, sharing challenges, and seeking advice fosters a collaborative learning environment. Attend local meetups or virtual events if you can to connect with professionals in the field, gaining insights and building relationships that can prove invaluable throughout your journey. - -## Think about you and your project - -There are many ways to go about picking the ideal backend language for you. If there is anything you should take home with you after reading this article, it is that most languages are equivalent in the sense that you’ll be able to do pretty much everything with any of them. - -So what criteria can you use to pick the “right one” for you? - -The questions you should also be asking yourself are: - -- What’s your preference for a language? Do you like Object Oriented Programming (OOP) or are you more of a functional programming type of dev? Do you like statically typed programming languages or loosely typed ones? Personal preferences should also play an important role at the time of picking your ideal programming language. -- What does my project need? After all, project requirements sometimes dictate technology. Keep that in mind, check if the project’s needs and your personal preferences align, and try to weigh in pros and cons if they don’t. - -In the end, personal preference and actual project requirements (if you have any) are very important, because both will influence how much you enjoy (or don’t enjoy) the learning process. - -## Crafting a Portfolio to Display Your Backend Skills: - -As you accumulate skills and knowledge, showcase your journey through a well-crafted portfolio. Include projects that highlight your backend skills, demonstrating your ability to - design databases, implement server-side logic, and integrate with client side technologies. Whether it's a dynamic web application, a RESTful API, or a data-driven project, your portfolio becomes a tangible representation of your backend development capabilities for potential employers or collaborators. - -When it comes to deciding where to publish this portfolio, you have some options, such as directly on your GitHub profile (if you have one), or perhaps on your own personal website where you can share some design thoughts about each project along with the code. - -In the end, the important thing is that you should be sharing your experience somewhere, especially when you don’t have working experience in the field. - -## Conclusion - -In the end, there are many backend programming languages to choose from, and what language you go for, is up to you and your particular context/needs. All I can do is guide you to the door, but you have to cross it yourself. Some interesting options are: - -- Python with its English-like syntax. -- Java with its formal syntax and enterprise support. -- JavaScript with its flexibility and ability to jump between frontend and backend. -- PHP with its proven record of success. -- And Go, with its performance and scalability focus. - -You’re the one who gets to decide, but just know that no matter what you choose, getting started in backend development is a one-way street. You’ll be learning from this moment on, and you’ll be jumping from one language to the other as the field evolves. - -Remember that there is a very detailed version of a [backend roadmap here](https://roadmap.sh/backend), it might be a great place to get started! And if you’re also interested in frontend development, there is an [equally handy roadmap](https://roadmap.sh/frontend) here as well! diff --git a/src/data/guides/backend-project-ideas.md b/src/data/guides/backend-project-ideas.md deleted file mode 100644 index 2dba6b724..000000000 --- a/src/data/guides/backend-project-ideas.md +++ /dev/null @@ -1,464 +0,0 @@ ---- -title: '20 Backend Project Ideas to take you from Beginner to Pro' -description: 'Seeking backend projects to enhance your skills? Explore our top 20 project ideas, from simple apps to complex systems. Start building today!' -authorId: fernando -excludedBySlug: '/backend/project-ideas' -seo: - title: '20 Backend Project Ideas to take you from Beginner to Pro' - description: 'Seeking backend projects to enhance your skills? Explore our top 20 project ideas, from simple apps to complex systems. Start building today!' - ogImageUrl: 'https://assets.roadmap.sh/guest/backend-project-ideas-zxutw.jpg' -relatedTitle: "Other Guides" -relatedGuidesId: backend -isNew: false -type: 'textual' -date: 2024-05-09 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Fresh ideas for backend projects.](https://assets.roadmap.sh/guest/backend-project-ideas-zxutw.jpg) - -As backend developers, showcasing our work to others is not straightforward, given that what we do is not very visible. - -That said, having a project portfolio, even as backend developers, it’s very important, as it can lead to new job opportunities. - -As an added bonus, the experience you get out of the entire process of building the apps for your portfolio will help you improve your coding skills. - -Let’s take a look at 20 of the best backend projects you can work on to improve both your project portfolio and to learn [backend development](https://roadmap.sh/backend). - -Keep in mind that these project ideas are organized from easiest to hardest to complete, and the entire list should take you at least a year to complete, if you’re not rushing the process. - -So sit back, grab a cup of your favorite hot drink, and let’s get started! - -## 1. Personal Blogging Platform API - -**Difficulty**: Easy - -**_Skills and technologies used_**: CRUD for main operations, databases (SQL or NoSQL), server-side RESTful API. - -![Blogging Platform API](https://assets.roadmap.sh/guest/blogging-platform-api.png) - -Let’s start with a very common one when it comes to backend projects. - -This is a RESTful API that would power a personal blog. This implies that you’d have to create a backend API with the following responsibilities: - -- Return a list of articles. You can add filters such as publishing date, or tags. -- Return a single article, specified by the ID of the article. -- Create a new article to be published. -- Delete a single article, specified by the ID. -- Update a single article, again, you’d specify the article using its ID. - -And with those endpoints you’ve covered the basic CRUD operations (**C**reate, **R**ead, **U**pdate and **D**elete). - -As a recommendation for techstack, you could use [Fastify](https://fastify.dev/) as the main [backend framework](https://roadmap.sh/backend/frameworks) if you’re going with Node, or perhaps [Django](https://www.djangoproject.com/) for Python or even [Ruby on Rails](https://rubyonrails.org/) or [Sinatra](https://sinatrarb.com/) for Ruby. As for your database, you could use [MongoDB](https://www.mongodb.com/) if you want to try NoSQL or [MySQL](https://www.mysql.com/) if you’re looking to get started with relational databases first. - -## 2. To-Do List API - -**_Difficulty_**: Easy - -**_Skills and technologies used_**: REST API design, JSON, basic authentication middleware. - -![To-Do List API](https://assets.roadmap.sh/guest/todo-list-api-bsrdd.png) - -We’re continuing with the APIs for our backend project ideas, this time around for a To-Do application. Why is it different from the previous one? - -While the previous project only focused on the main CRUD operations, here we’ll add some more interesting responsibilities, such as: - -1. An authentication logic, which means you’ll have to keep a new table of users and their credentials -2. You’ll have to create both users and tasks. -3. You’ll also have to be able to update tasks (their status) and even delete them. -4. Get a list of tasks, filter them by status and get the details of each one. - -You’re free to implement this with whatever programming language and framework you want, however, you could continue using the same stack from the previous project. - -## 3. Weather API Wrapper Service - -**_Difficulty_**: Easy - -**_Skills and technologies used_**: Third-party API integration, caching strategy, environment variable management. - -![Weather API Wrapper Service](https://assets.roadmap.sh/guest/weather-api-f8i1q.png) - -Let’s take our API magic to the next level with this new backend project. Now instead of just relying on a database, we’re going to tackle two new topics: - -- Using external services. -- Adding caching through the use of a quick in-memory storage. - -As for the actual weather API to use, you can use your favorite one, as a suggestion, here is a link to [Visual Crossing’s API](https://www.visualcrossing.com/weather-api), it’s completely FREE and easy to use. - -Regarding the in-memory cache, a pretty common recommendation is to use [Redis](https://redis.io/), you can read more about it [here](https://redis.io/docs/manual/client-side-caching/), and as a recommendation, you could use the city code entered by the user as the key, and save there the result from calling the API. - -At the same time, when you “set” the value in the cache, you can also give it an expiration time in seconds (using the EX flag on the [SET command](https://redis.io/commands/set/)). That way the cache (the keys) will automatically clean itself when the data is old enough (for example, giving it a 12-hours expiration time). - -## 4. Expense Tracker API - -**_Difficulty_**: Easy - -**_Skills and technologies used_**: Data modeling, user authentication (JWT). - -![Expense Tracker API](https://assets.roadmap.sh/guest/expense-tracker-api-m72p5.png) - -For the last of our “easy” backend projects, let’s cover one more API, an expense tracker API. This API should let you: - -- Sign up as a new user. -- Generate and validate JWTs for handling authentication and user session. -- List and filter your past expenses. You can add the following filters: - - Past week. - - Last month. - - Last 3 months. - - Custom (to specify a start and end date of your choosing). -- Add new expenses. -- Remove existing expenses. -- Update existing expenses. - -Let’s now add some constraints: - -- You’ll be using [JWT](https://itnext.io/demystifying-jwt-a-guide-for-front-end-developers-ead6574531c3) (JSON Web Token) to protect the endpoints and to identify the requester. -- For the different expense categories, you can use the following list (feel free to decide how to implement this as part of your data model): - - Groceries - - Leisure - - Electronics - - Utilities - - Clothing - - Health - - Others. - -As a recommendation, you can use MongoDB or an ORM for this project, such as [Mongoose](https://mongoosejs.com/) (if you’re using JavaScript/Node for this). - -From everything you’ve done so far, you should feel pretty confident next time you have to build a new API. - -## 5. Markdown Note-taking App - -**_Difficulty_**: Moderate - -**_Skills and technologies used_**: Text processing, Markdown libraries, persistent storage, REST API with file upload. - -![Markdown Note-taking App](https://assets.roadmap.sh/guest/markdown-note-taking-app-tymi3.png) - -You’ve been building APIs all this time, so that concept alone should not be a problem by now. However, we’re increasing the difficulty by allowing file uploads through your RESTful API. You’ll need to understand how that part works inside a RESTful environment and then figure out a strategy to store those files while avoiding name collisions. - -You’ll also have to process the text in the following ways: - -- You’ll provide an endpoint to check the grammar of the note. -- You’ll also provide an endpoint to save the note that can be passed in as Markdown text. -- Return the HTML version of the Markdown note (rendered note) through another endpoint. - -As a recommendation, if you’re using JavaScript for this particular project, you could use a library such as [Multer](https://www.npmjs.com/package/multer), which is a Node.js module. - -## 6. URL Shortening Service - -**_Difficulty_**: Moderate - -**_Skills and technologies used_**: Database indexing, HTTP redirects, RESTful endpoints - -![URL Shortening Service](https://assets.roadmap.sh/guest/url-shortening-service-c1nzi.png) - -We’re now moving away from your standard APIs, and tackling URL shortening. This is a very common service, which allows you to shorten very long URLs, especially when looking to share them on social media or make them easily memorable. - -For this project idea let’s focus on the following features, which you should be more than capable of implementing on your local environment, no matter your OS. - -- Ability to pass a long URL as part of the request and get a shorter version of it. You’re free to decide how you’ll perform the shortening . -- Save the shorter and longer versions of the URL in the database to be used later during redirection. -- Configure a catch-all route on your service that gets all the traffic (no matter the URI used), finds the correct longer version and performs a redirection so the user is seamlessly redirected to the proper destination. - -## 7. Real-time Polling App - -**_Difficulty_**: Moderate - -**_Skills and technologies used_**: WebSockets, live data updates, state management - -![Real-time Polling App](https://assets.roadmap.sh/guest/realtime-polling-app-8qx5h.png) - -Time to leave APIs alone for a while and focus on real-time interactions, another hot topic in web development. In fact, let’s try to use some sockets. - -Sockets are a fantastic way of enabling 2-way communication between two or more parties (systems) with very few lines of code. Read more about sockets [here](https://www.digitalocean.com/community/tutorials/understanding-sockets). - -That being said, we’re building both a client and a server for this project. The client can easily be a CLI (Command Line Interface) tool or a terminal program that will connect to the server and show the information being returned in real-time. - -The flow for this first socket-based project is simple: - -- The client connects to the server and sends a pre-defined request. -- The server upon receiving this request, will send, through the same channel, an automatic response. - -While the flow might seem very similar to how HTTP-based communication works, the implementation is going to be very different. Keep in mind that from the client perspective, the request is sent, and there is no waiting logic, instead, the client will have code that gets triggered when the message from the server is received. - -This is a great first step towards building more complex socket-based systems. - -## 8. Simple E-commerce API - -**_Difficulty_**: Moderate - -**_Skills and technologies used_**: Shopping cart logic, payment gateway integration (Stripe, PayPal), product inventory management - -![Simple E-commerce API](https://assets.roadmap.sh/guest/simple-ecommerce-api-thzqo.png) - -Back to the world of APIs, this time around we’re pushing for a logic-heavy implementation. - -For this one, you’ll have to keep in mind everything we’ve been covering so far: - -- JWT authentication to ensure many users can interact with it. -- Interaction with external services. Here you’ll be integrating with payment gateways such as Stripe. -- A complex data model that can handle products, shopping carts, and more. - -With that in mind, let’s take a look at the responsibilities of this system: - -- JWT creation and validation to handle authorization. -- Ability to create new users. -- Shopping cart management, which involves payment gateway integration as well. -- Product listings. -- Ability to create and edit products in the database. - -This project might not seem like it has a lot of features, but it compensates in complexity, so don’t skip it, as it acts as a great progress check since it’s re-using almost every skill you’ve picked up so far. - -## 9. Fitness Workout Tracker - -**_Difficulty_**: Moderate - -**_Skills and technologies used_**: User-specific data storage, CRUD for workout sessions, date-time manipulation. - -![Fitness Workout Tracker](https://assets.roadmap.sh/guest/fitness-workout-tracker-82uux.png) - -This backend project is not just about taking in user-generated notes, but rather, about letting users create their own workout schedules with their own exercises and then go through them, checking the ones they’ve done, and the ones they haven't. - -Making sure you also give them the space to add custom notes, with remarks about how the exercise in question felt and if they want to tweak it in the future. - -Keep in mind the following responsibilities for this backend project: - -- There needs to be a user sign-up and log-in flow in this backend system, as many users should be able to use it. -- There needs to be a secure JWT flow for authentication. -- The system should let users create workouts composed of multiple exercises. -- For each workout, the user will be able to update it and provide comments on it. -- The schedule the user creates needs to be associated to a specific date, and any listing of active or pending workouts needs to also be sorted by date (and time if you want to take it one step further). -- There should also be a report of past workouts, showing the percentage of finished workouts during the queried period. - -The data model for this one can also be complex, as you’ll have predefined exercises that need to be grouped into workout sessions, and those can then have associated comments (input from the user). - -![Fitness Workout Tracker Data Model](https://assets.roadmap.sh/guest/fitness-workout-tracker-datamodel-5mrkq.png) - -Consider the benefits of using a structured model here vs something document-based, such as MongoDB and pick the one that feels better for your implementation. - -## 10. Recipe Sharing Platform - -**_Difficulty_**: Moderate - -**_Skills and technologies used_**: File uploads and image processing (like Sharp), user permissions, complex querying - -![Recipe Sharing Platform](https://assets.roadmap.sh/guest/recipe-sharing-platform-jzs08.png) - -While this project might feel a lot like the first one, the personal blogging platform, we’re taking the same concept, and then adding a lot more on top of it. - -We’re building a RESTful API (or rather several) that will let you perform the following actions: - -- Access a list of recipes. You should be able to filter by keywords (text input), publication date, by chef, and by labels. Access to this endpoint should be public. -- The list should be paginated, and as part of the response on every page. -- Users should be able to sign up as chefs to the system to upload their own recipes. -- A JWT-secured login flow must be present to protect the endpoints in charge of creating new recipe posts. -- Images uploaded as part of the recipe should be processed to be re-sized into a standard size (you pick the dimensions). You can use a library such as [Sharp](https://sharp.pixelplumbing.com/) for this. - -## 11. Movie Reservation System - -**_Difficulty_**: Difficult - -**_Skills and technologies used_**: Relational data modeling (SQL), seat reservation logic, transaction management, schedule management. - -![Movie Reservation System](https://assets.roadmap.sh/guest/movie-reservation-system-5823e.png) - -There are very expensive pre-made tools that handle all this logic for companies, and the following diagram shows you a glimpse of that complexity. - -As backend projects go, this one is a great example of the many different problems you might need to solve while working in web development. - -A movie reservation system should allow any user to get tickets and their associated seats for any movie playing the specific day the user is looking to attend. This description alone already provides a lot of features and constraints we have to keep in mind: - -- We’re going to have a list of movies (and maybe theaters as well). -- Each movie will have a recurring schedule for some time and then it’ll be taken out to never return. -- Users should be able to list movies, search for them and filter by dates, genres and even actors. -- Once found, the user should be able to pick the seats for their particular movie of choice, and for their date of choice. -- This leads us to you having to keep a virtual representation of your movie theater to understand seating distribution and availability. -- In the end, the user should also be able to pay using an external payment gateway such as Stripe (we’ve already covered this step in the past). - -## 12. Restaurant Review Platform (API) with automatic NLP analysis - -**_Difficulty_**: Difficult - -**_Skills and technologies used_**: RESTful API, In-memory database (for live leaderboard), SQL, Natural Language Processing to auto-label positive and negative comments. - -![Restaurant Review Platform](https://assets.roadmap.sh/guest/restaurant-review-platform-26c1f.png) - -Now this project takes a turn into the land of noSQL and AI by leading with user input. The aim of this particular backend project is to provide a very simple API that will let users: - -- Input their own review of a restaurant (ideally, the API should request the restaurant’s ID to make sure users are reviewing the correct one). -- Keep a leaderboard of restaurants with a generic positive or negative score, based on the type of reviews these restaurants get. For this, you can use Redis as an in-memory leaderboard to have your API query it, instead of hitting the database you’re using. This also implies that you’ll have to keep the leaderboard updated on Redis as well (as a hint: look for type [SortedSet](https://redis.io/docs/data-types/sorted-sets/) data type to understand how to do this). -- Perform NLP (Natural Language Processing) on the user’s text portion of the review, to understand if it’s a positive one or a negative one. -- Use the result of the NLP as a scoring system for the leaderboard. - -As a recommendation, you might want to use Python on this project, as there tend to be more libraries around NLP for this language. - -## 13. Multiplayer Battleship Game Server - -**Difficulty**: Difficult - -**_Skills and technologies used_**: Game state synchronization, low-latency networking, concurrency control. - -![Multiplayer Battleship Game Server](https://assets.roadmap.sh/guest/multiplayer-battleship-lv5oj.png) - -For this project you’re not going to build a full game from scratch, so don’t worry. - -You will however, build a game server. Your game server will have to maintain the internal state of each player’s board, and it should also enable communication between them by broadcasting their actions and results. Since we have “low-latency networking” as a constraint here, the logical implementation would be through the use of Sockets (so if you haven’t done it yet, go back to project 7 and work on it first). - -You’re free to pick the programming language you feel more comfortable with, however, keep the mind that you’ll have to: - -- Keep track of the player's state and game state. -- Enable 2-way communication between players and server. -- Allow players to join the game and set up their initial state somehow. - -This can be a very fun project to work on, even if you’re “just” building a terminal version of this multiplayer game, as you’ll be using several of the concepts and technologies covered so far on this list. - -## 14. Database Backup CLI utility - -**_Difficulty_**: Difficult - -**_Skills and technologies used_**: Advanced SQL, Database fundamentals, CLI development, Node.js (for CLI) - -![Database Backup CLI utility](https://assets.roadmap.sh/guest/database-backup-cli-jwrcj.png) - -We’re now moving away from the API world for a while, and into the world of command line interfaces, which is another very common space for backend developers to work on. - -This time around, the project is a CLI utility to back up an entire database. - -So for this project, you’ll be creating a command line utility that takes the following attributes: - -- **Host:** the host of your database (it can be localhost or anything else). -- **Username**: the utility will need a username to login and query the database. -- **Password**: same with the password, usually databases are protected this way. -- **DB Name**: the name of the database to backup. We’re backing up the entire set of tables inside this database. -- **Destination folder**: the folder where all the dump files will be stored. - -With all that information, your utility should be able to connect to the database, pull the list of tables, and for each one understand its structure and its data. In the end, the resulting files inside the destination folder should have everything you need to restore the database on another server simply by using these files. - -Finally, if you haven’t heard of it yet, you might want to check out the [SHOW CREATE TABLE](https://dev.mysql.com/doc/refman/8.3/en/show-create-table.html) statement. - -## 15. Online Code Compiler API - -**_Difficulty_**: Difficult - -**_Skills and technologies used_**: Sandboxing code execution, integration with compilers, WebSocket communication. - -![Online Code Compiler API](https://assets.roadmap.sh/guest/online-code-compiler-c7xjb.png) - -For this project, you’ll be building the backend of a remote code execution application. In other words, your APIs will allow you to receive source code written using a specific language of choice (you can pick the one you want, and only allow that one), run it and then return the output of that execution. - -Of course, doing this without any restrictions is not worth it for being in the “difficult” section of this list, so let’s kick it up a notch: - -- The code execution should be done inside a safe sandbox, which means that the code can’t hurt or affect the system it’s running on, no matter what the code or the logic dictates. -- On top of that, for long-running tasks, the API should also provide a status report containing the following information: -- Time running. -- Start time of the execution. -- Lines of code being executed. - -## 16. Messaging Platform Backend - -**_Difficulty_**: Difficult - -**_Skills and technologies used_**: Real-time messaging, end-to-end encryption, contact synchronization - -![Messaging Platform Backend](https://assets.roadmap.sh/guest/messaging-platform-backend-96jpw.png) - -Yes, we’re talking about a chat platform here. And as a backend developer you’re more than ready to implement both the server and the client application. - -This backend project would take project #7 to the next level, by implementing the following responsibilities: - -- Adding message encryption between client applications -- The ability to understand who’s online -- Understand if those users are interacting with you (a.k.a showing the “\[username\] is typing” message in real-time). -- Sending a message from one of the clients into the server should be broadcasted to the rest of the clients connected. - -As a recommendation for technology implementing this project, [Socket.io](http://socket.io) would be a perfect match. This means you’d be using JavaScript (node.js) for this. - -## 17. Content Delivery Network (CDN) Simulator - -**_Difficulty_**: Very Difficult - -**_Skills and technologies used_**: Load balancing algorithms, caching strategies, network latency simulation - -![Content Delivery Network (CDN) Simulator](https://assets.roadmap.sh/guest/cdn-simulator-lv7kl.png) - -For this particular backend project, we’re not going to focus on coding, but rather on backend tools and their configuration. A [CDN](https://aws.amazon.com/what-is/cdn/) (or Content Delivery Network) is a platform that allows you to serve static content (like text files, images, audio, etc) safely and reliably. - -Instead of having all files inside the same server, the content is replicated and distributed across a network of servers that can provide you with the files at any given point in time. - -The point of this project is for you to figure out a way to set up your own CDN keeping in mind the following points: - -- Use cloud servers (you can pick your favorite cloud provider for this) -- Configure a load balancer to distribute the load between all servers. -- Set up a caching strategy. - -Remember that all major cloud providers have a free tier that allows you to use all their services for some time. AWS for example, allows for a full year of free tier limited to the type of resources you can use. - -## 18. Time-tracking CLI for Freelancers - -**_Difficulty_**: Very Difficult - -**_Skills and technologies used_**: time tracking, interactive CLI, Day.js for time operations, reporting. - -![Time-tracking CLI for Freelancers](https://assets.roadmap.sh/guest/time-tracking-cli-freelancers-x7ldv.png) - -As freelancers, sometimes understanding what you’re working on, or understanding how much time you’ve spent on a particular project once it’s time to get paid, can be a challenge. - -So, with this command line interface tool, we’ll try to solve that pain for freelancers. The tool you’re developing should let you specify that you’re starting to work on a project, and once you’re done, you should also be able to say that you’ve stopped. - -On top of that, there should be an interactive reporting mode that should tell you the amount of time spent so far on a particular project (with the ability to filter by date and time), so you can know exactly how much to charge each client. - -From the user’s POV, you could have commands like this: - -- freelance start project1 -- freelance stop project2 - -And when in interactive mode, something like this should work: - -- report project1 since 1/2/24 - -The challenge on this backend project is not just the CLI itself, which you’ve built in the past, but the actual time tracking logic that needs to happen internally. You’ll be keeping track of small chunks of time associated with different backend projects, and once the report is requested, you need to properly query your DB and get only the right chunks, so you can later add them up and return a valid number. - -## 19. JS Obfuscator CLI utility - -**_Difficulty_**: Very Difficult - -**_Skills and technologies used_**: code obfuscation, batch processing of files using a CLI, Node.js. - -![JS Obfuscator CLI utility](https://assets.roadmap.sh/guest/js-obfuscator-utility-f0dfi.png) - -Code obfuscation happens when you turn a perfectly readable code into something that only a machine can understand, without changing the plain text nature of the file. In other words, you just make it impossible for a human to read and understand. - -Many tools do this in the JS ecosystem, it’s now your turn to create a new tool and perform the exact same action. As an added difficulty, you’ll be coding a tool that does this to an entire folder filled with files (not just one at the time). - -Make sure the output for each file is placed inside the same folder, with a “.obs.js” extension, and that you’re also navigating sub-folders searching for more files. - -Try to avoid libraries that already perform these exact same tasks, as you’ll be skipping through all the potential problems you can find, and effectively learning nothing from the experience. - -## 20. Web Scraper CLI - -**_Difficulty_**: Very Difficult - -**_Skills and technologies used_**: Web scraping, headless browsing, rules engine - -![Web Scraper CLI](https://assets.roadmap.sh/guest/webscraper-cli-ja5h2.png) - -A web scraper is a tool that allows you to navigate a website through code, and in the process, capture information from the presented web pages. - -As part of the last backend project of this list, you’ll be implementing your very own web scraper CLI tool. This tool will take input from the user with a list of preset commands, such as: - -- show code: to list the HTML code of the current page. -- navigate: to open a new URL -- capture: this will return a subsection of the HTML of the current page using the CSS selector you specify. -- click on: this command will trigger a click on a particular HTML element using a CSS selector provided. - -Feel free to add extra commands to make the navigation even more interactive. - -With the last of our backend project ideas, you’ve covered all the major areas involved in backend development and you’re more than ready to apply for a backend development job if you haven’t already. - -If you find a piece of technology that wasn’t covered here, you’ll have the skills required to pick it up in no time. diff --git a/src/data/guides/backend-technologies.md b/src/data/guides/backend-technologies.md deleted file mode 100644 index 4094a52af..000000000 --- a/src/data/guides/backend-technologies.md +++ /dev/null @@ -1,454 +0,0 @@ ---- -title: 'Top 10+ Backend Technologies to Use in @currentYear@: Expert Advice' -description: 'Looking for the best backend technologies in @currentYear@? Check out our expert list of top tools for developers.' -authorId: fernando -excludedBySlug: '/backend/technologies' -seo: - title: 'Top 10+ Backend Technologies to Use in @currentYear@: Expert Advice' - description: 'Looking for the best backend technologies in @currentYear@? Check out our expert list of top tools for developers.' - ogImageUrl: 'https://assets.roadmap.sh/guest/backend-technologies-pnof4.jpg' -relatedTitle: "Other Guides" -relatedGuidesId: backend -isNew: false -type: 'textual' -date: 2024-08-27 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Best backend development technologies.](https://assets.roadmap.sh/guest/backend-technologies-pnof4.jpg) - -Backend technologies are the key to building robust and scalable applications. They power all platforms and products on the web without even being visible to the users. - -While backend programming languages form the foundation of backend development, they aren't enough on their own. Understanding and leveraging the right backend technologies can significantly enhance your development workflow and application performance. - -As a [backend developer](https://roadmap.sh/backend), you’ll be faced with too many options while trying to define your backend technology stack, and that can feel overwhelming. - -So, in this article, we’re going to cover the best backend technologies in the following categories: - -- Databases -- Version control systems -- Containerization and orchestration -- Cloud platforms -- APIs & Web Services -- Caching systems -- Message brokers -- Authentication and Authorization systems -- CI/CD -- Monitoring & Logging - -These should help you stay up-to-date or reach the required level to succeed as a backend developer. - -## Databases - -![Databases](https://assets.roadmap.sh/guest/databases-4a1kz.png) - -We can’t have a list of backend technologies to learn without covering databases. After all, databases are a core piece of the best backend technologies in use today and the backbone of any application, providing the necessary storage and retrieval of data. Choosing the right type of database depends on your application's requirements, such as data consistency, scalability, and complexity. - -### SQL Databases - -SQL databases (or relational databases as they’re also called) bring structure to your data and a standard querying language known as SQL. - -#### PostgreSQL - -PostgreSQL is an advanced open-source relational database known for its reliability and extensive feature set. It supports a wide range of data types and complex queries, making it ideal for applications that require ACID compliance and advanced data handling capabilities. PostgreSQL is commonly used in financial systems, data warehousing, and applications needing strong data integrity and complex reporting. - -PostgreSQL also offers robust support for JSON and JSONB data types, enabling seamless integration of relational and NoSQL capabilities within a single database system. Its powerful indexing mechanisms ensure efficient query performance even with large datasets. - -Additionally, PostgreSQL provides advanced security features like row-level security and multi-factor authentication, making it a secure choice for handling sensitive data. - -#### MySQL - -MySQL is a widely used open-source SQL database praised for its speed, reliability, and ease of use. It is particularly popular backend technology for web applications and online transaction processing (OLTP) due to its performance and robust community support. MySQL is often the database of choice for content management systems, e-commerce platforms, and logging applications. - -MySQL also supports a variety of storage engines, including InnoDB, which provides ACID compliance, foreign key support, and transaction-safe operations, making it suitable for a wide range of applications. - -Its replication capabilities, including master-slave and group replication, ensure high availability and scalability for large-scale deployments. Additionally, MySQL offers advanced security features such as data encryption, user authentication, and role-based access control, enhancing its suitability for handling sensitive data. - -#### Microsoft SQL Server - -SQL Server is a relational database management system from Microsoft that offers great performance, scalability, and deep integration with other Microsoft products. It provides comprehensive tools for database management, including advanced analytics and business intelligence features. SQL Server is ideal for enterprise-level applications, data warehousing, and environments where integration with Microsoft services, such as Azure, is beneficial. - -MSSQL Server also includes robust security features, such as transparent data encryption, dynamic data masking, and advanced threat protection, making it a trusted choice for handling sensitive data. It supports a wide range of data types, including spatial and XML data, and offers powerful indexing and query optimization techniques to ensure efficient data retrieval and processing. - -SQL Server's integration with Visual Studio and other Microsoft development tools helps to streamline the development process. - -#### SQLite - -SQLite is a self-contained, serverless, and zero-configuration database engine known for its simplicity and ease of use. It is lightweight and efficient, making it perfect for small to medium-sized applications, mobile apps, desktop applications, and prototyping. SQLite is embedded within the application, eliminating the need for a separate database server, which simplifies deployment and maintenance. Its single-disk file format makes it highly portable across various operating systems and platforms. -SQLite's efficient memory and disk usage allow it to perform well even on devices with limited resources, such as IoT devices and embedded systems. - -This makes SQLite an excellent choice for applications where simplicity, reliability, and low overhead are essential. - -### NoSQL Databases - -On the other hand, NoSQL databases allow for more flexibility by removing the need for a fixed schema and structure to your data. Each solution presented here covers a different type of unstructured database, and it’s up to you to decide if that focus actually makes sense for your business logic or not. - -#### MongoDB - -MongoDB is a document-oriented database that offers flexibility and scalability. It handles unstructured data with ease, making it ideal for applications with large-scale data and real-time analytics. MongoDB is commonly used in content management systems, e-commerce platforms, and applications that require a dynamic schema. Its ability to store data in JSON-like documents allows for easy data retrieval and manipulation. - -#### DynamoDB - -DynamoDB is a fully managed NoSQL database service provided by AWS. It is designed for high-performance applications requiring seamless scalability and high availability. DynamoDB is best suited for high-traffic web applications, gaming, and IoT applications. Its serverless nature means it can automatically scale up or down based on demand, ensuring consistent performance and cost-efficiency. - -#### Cassandra - -Cassandra is an open-source distributed NoSQL database known for its high availability and fault tolerance without compromising performance. It is ideal for large-scale applications and real-time big data analytics. Cassandra's distributed architecture makes it perfect for environments requiring continuous availability and the ability to handle large amounts of data across multiple nodes. It is commonly used in social media platforms, recommendation engines, and other data-intensive applications. - -## Version Control Systems - -![Version Control Systems](https://assets.roadmap.sh/guest/version-control-flow-bzojr.png) - -Version control systems are essential for managing changes to source code over time, allowing multiple developers to collaborate effectively and maintain a history of changes. - -### Git - -When it comes to picking the right version control tool, Git is the most widely used one. It provides a powerful, flexible, and distributed model for tracking changes. Git’s architecture supports nonlinear development, allowing multiple branches to be created, merged, and managed independently. This makes Git essential for code collaboration and version tracking, making it a foundational tool for any developer. - -Let’s go through some of the key benefits that make Git one of the leading backend technologies in web development. - -#### Distributed Version Control - -Unlike centralized version control systems (CVCS) where a single central repository holds the entire project history, Git allows each developer to have a complete copy of the repository, including its history. This decentralized approach enhances collaboration and ensures that work can continue even if the central server is down. - -#### Branching and Merging - -**Branching**: Git’s lightweight branching model allows developers to create, delete, and switch between branches effortlessly. This facilitates isolated development of features, bug fixes, or experiments without impacting the main codebase. - -**Merging**: Git provides powerful merging capabilities to integrate changes from different branches. Tools like merge commits and rebasing help manage and resolve conflicts, ensuring a smooth integration process. - -#### Performance - -Git is designed to handle everything from small to very large projects with speed and efficiency. Its performance for both local operations (like committing and branching) and remote operations (like fetching and pushing changes) is optimized, making it suitable for high-performance needs. - -#### Commit History and Tracking - -Commit Granularity: Git encourages frequent commits, each with a descriptive message, making it easier to track changes, understand the project history, and identify when and why a change was made. - -**History Viewing**: Commands like git log, git blame, and git bisect allow developers to explore the project’s history, pinpoint the introduction of bugs, and understand the evolution of the codebase. - -#### Collaboration - -While strictly not part of Git’s feature set, these functionalities enhance the basic set of features provided by the version control system. - -**Pull Requests**: Platforms like GitHub, GitLab, and Bitbucket build on Git’s capabilities, offering features like pull requests to facilitate code reviews and discussions before integrating changes into the main branch. - -**Code Reviews**: Integrating with continuous integration (CI) systems, Git platforms enable automated testing and code quality checks, ensuring that changes meet project standards before merging. - -#### Staging Area - -Git’s staging area (or index) provides an intermediate area where changes can be formatted and reviewed before committing. This allows for more granular control over what changes are included in a commit. - -### GitHub - -GitHub is a web-based platform that leverages Git for version control. It provides an extensive list of collaborative features, including (as already mentioned) pull requests, code reviews, and project management tools. - -#### Key Features and Benefits - -**Pull Requests and Code Reviews**: Facilitate discussions around proposed changes before integrating them into the main codebase. Developers can review code, leave comments, and suggest improvements. Built-in tools for reviewing code changes ensure collaborations are following coding standards and catch potential issues early. - -**Project Management**: GitHub Issues allow tracking of bugs, enhancements, and tasks. Milestones help in organizing issues into targeted releases or sprints. Kanban-style boards provide a visual way to manage tasks, track progress, and organize workflows. - -**Continuous Integration and Deployment**: Automate workflows for CI/CD, testing, deployment, and more. GitHub Actions supports custom scripts and pre-built actions to streamline DevOps processes. - -**Community and Collaboration**: Developers can host static websites directly from a GitHub repository with Github Pages, they’re ideal for project documentation or personal websites. Integrated wikis can be used for detailed project documentation. And through forking, starring, and following repositories the platform encourages collaboration and knowledge sharing. - -GitHub’s extensive features and strong community support make it the de facto choice for many companies and developers, both for open-source and private projects. - -### GitLab - -GitLab is a web-based platform for version control using Git, known for its robust CI/CD pipeline integration. It offers a comprehensive set of tools for the entire DevOps lifecycle, making it suitable for continuous integration, deployment, and monitoring. - -#### Key Features and Benefits - -**Integrated CI/CD**: Built-in continuous integration and continuous deployment pipelines allow backend developers to automate building, testing, and deploying code changes. With Gitlab they can even automatically configure CI/CD pipelines, deploy applications, and monitor performance, all through the same platform. - -**Security and Compliance**: Gitlab provides key security capabilities for backend development: built-in static and dynamic application security testing (SAST/DAST). - -**Collaboration and Communication**: Instead of Pull Requests like Github, Gitlab provides the concept of “Merge Requests”: a simplified code review process with inline comments and suggestions. - -GitLab’s all-in-one platform makes it an excellent choice for teams looking to streamline their DevOps processes and improve collaboration and productivity. - -### Bitbucket - -Bitbucket is a Git-based source code repository hosting service that provides both commercial plans and free accounts for small teams. It integrates seamlessly with Atlassian products like Jira and Trello, making it a great choice for teams already using these tools. - -**Repository Hosting**: Bitbucket supports both Git and Mercurial version control systems. And it offers unlimited private repositories for teams. - -**Integration with Atlassian Products**: Seamlessly integrates with Jira for issue tracking and project management. It can create branches from Jira issues and view development progress which is a fantastic integration for big teams using both tools. If, on the other hand, you’re using Trello, it can connect to Trello’s boards for visual task management and tracking. - -**Continuous Integration and Deployment**: Integrated CI/CD service for automating builds, tests, and deployments. It can be configured with a YAML file for custom workflows. - -**Security and Permissions**: Control who accesses specific branches to enforce workflows and protect critical branches. You can even enhance security with two-factor authentication. - -Bitbucket’s integration with Atlassian’s suite of products, along with its robust CI/CD capabilities, make it an attractive option for teams seeking a tightly integrated development and project management environment. - -## Containerization and Orchestration - -![Containerization and Orchestration](https://assets.roadmap.sh/guest/containers-and-orchestrators-jb6xj.png) - -While backend developers aren’t always directly involved in the deployment process, understanding the basics of containerization and orchestration can help them work and interact with the team in charge of devops (who usually set up these CI/CD pipelines). - -While this is not an exhaustive list of backend technologies, the two main ones to learn about are: - -### Docker - -Docker is a platform for developing, shipping, and running applications in containers. Containers package software and its dependencies, ensuring it runs consistently across different environments. Docker simplifies application deployment and testing, making it ideal for microservices architectures and continuous integration/continuous deployment (CI/CD) pipelines. - -### Kubernetes - -Kubernetes is an open-source system for automating the deployment, scaling, and management of containerized applications. It orchestrates containers across clusters of machines, providing high availability, scalability, and efficient resource utilization. Kubernetes is perfect for complex, large-scale applications requiring robust infrastructure management and automated scaling. - -## Cloud Platforms - -![Cloud Platforms](https://assets.roadmap.sh/guest/cloud-providers-ownec.png) - -Cloud platforms provide a range of services and infrastructure that allow developers to deploy, manage, and scale applications without maintaining physical servers. Mind you, the “cloud” is nothing else than someone else’s servers that you don’t have to manage. - -These platforms all offer very similar types of managed services (each with its own flavor) that allow you to set up powerful and scalable infrastructures with a few clicks. - -### Amazon Web Services (AWS) - -Amazon Web Services (AWS) is a very complete cloud computing platform offered by Amazon. It provides a broad range of services, including computing power, storage solutions, and databases, catering to various needs and applications. - -#### Key Characteristics of AWS - -**Scalability**: AWS provides scalable solutions that allow businesses to easily adjust resources based on demand. -Global Reach: With data centers located worldwide, AWS offers high availability and low latency for global applications. - -**Diverse Service Offerings**: AWS offers a wide range of services, including EC2 for computing, S3 for storage, and RDS for databases. - -**Security and Compliance**: AWS provides robust security features and complies with numerous industry standards and regulations. - -**Cost Management**: Flexible pricing models and cost management tools help businesses optimize their cloud spending. - -### Google Cloud Platform (GCP) - -Google Cloud Platform (GCP) is a suite of cloud computing services provided by Google. Like AWS and Microsoft Azure, GCP offers a variety of services, including computing power, storage, machine learning, and data analytics. - -#### Key Characteristics of GCP - -**AI and Machine Learning**: GCP excels in providing advanced AI and machine learning tools, leveraging Google's expertise. - -**Big Data and Analytics**: GCP offers powerful analytics tools, including BigQuery, for handling large-scale data processing. - -**Networking**: GCP provides a robust and secure global network infrastructure. - -**Integration with Google Services**: Seamless integration with Google Workspace and other Google services enhances productivity and collaboration. - -**Open Source Support**: GCP supports various open-source technologies, promoting flexibility and innovation. - -### Microsoft Azure - -Microsoft Azure is a cloud computing service created by Microsoft, offering a wide range of cloud services, including those for computing, analytics, storage, and networking. - -#### Key Characteristics of Microsoft Azure - -**Integration with Microsoft Products**: Azure offers seamless integration with popular Microsoft software and services. - -**Hybrid Cloud Capabilities**: Azure supports hybrid cloud environments, enabling smooth integration between on-premises and cloud resources. - -**Comprehensive Service Range**: Azure provides a broad spectrum of services, including Azure Virtual Machines, Azure SQL Database, and Azure DevOps. - -**Enterprise-Grade Security**: Azure emphasizes security with advanced features and compliance with industry standards. - -**Developer and IT Pro Tools**: Azure offers a wide range of tools for developers and IT professionals, including Visual Studio and Azure DevOps. - -At a high level, all of these providers are very similar to each other, to the point where backend developers experienced in one of them, can extrapolate their understanding of the environment into others with minimum ramp-up time. - -## APIs and Web Services - -![APIs and Web Services](https://assets.roadmap.sh/guest/rest-vs-graphql-vs-grpc-tp40c.png) - -APIs (or Application Programming Interfaces) and web services are another mandatory incorporation to the list of top backend technologies any developer should keep in mind. They enable communication between different software systems. - -The three most common types of APIs right now, are REST, GraphQL and gPRC, let’s take a closer look at each one of them. - -### REST - -REST is a standard architecture for web services, known for its simplicity and scalability. It operates on stateless principles and uses standard HTTP methods (GET, POST, PUT, DELETE) to perform CRUD (Create, Read, Update, Delete) operations. RESTful APIs are typically used to access and manipulate web resources using URLs. - -REST is ideal for web applications and services due to its ease of implementation and broad compatibility with various web technologies. It is commonly used for developing APIs for web and mobile applications, providing endpoints that clients can interact with to perform various operations. RESTful APIs are also ideal for integrating with third-party services, enabling data exchange and interaction between different systems. - -#### Key Characteristics of REST - -**Statelessness**: Each request from a client contains all the information needed to process the request, without relying on stored context on the server. - -**Uniform Interface**: REST APIs follow standard conventions, making them easy to understand and use. This includes using standard HTTP methods and status codes. - -**Client-Server Architecture**: Separates the client and server concerns, improving scalability and flexibility. Clients handle the user interface and user experience, while servers handle data storage and business logic. - -**Cacheability**: Responses from REST APIs can be cached to improve performance, reducing the need for repeated requests. - -**Layered System**: REST allows for a layered system architecture, enabling intermediaries like load balancers and proxy servers to enhance security, performance, and scalability. - -If you’d like to know more about REST, you can read the full definition directly from [its source](https://ics.uci.edu/~fielding/pubs/dissertation/rest_arch_style.htm). - -### GraphQL - -GraphQL is a query language for APIs and a runtime for executing those queries by using a type system you define for your data. Unlike REST, where multiple endpoints return fixed data structures, GraphQL allows clients to request exactly the data they need. This flexibility reduces the amount of data transferred over the network and can significantly improve performance. - -GraphQL is ideal for applications with complex and dynamic data requirements. - -#### Key Characteristics of GraphQL - -**Declarative Data Fetching**: Clients specify the structure of the response, ensuring they receive only the data they need. - -**Strongly Typed Schema**: The API schema is strongly typed, providing clear and detailed documentation of available data and operations. - -**Single Endpoint**: Unlike REST, GraphQL uses a single endpoint to serve all requests, simplifying the API architecture. - -**Real-time Data**: Supports real-time updates through subscriptions, enabling clients to receive live data changes. - -### gRPC - -gRPC is a high-performance, open-source RPC (Remote Procedure Call) framework developed by Google. gRPC is designed for low-latency, high-throughput communication, making it suitable for microservices architectures and real-time communication systems. - -gRPC is ideal for applications that require efficient, reliable, and bi-directional communication. - -#### Key Characteristics of gRPC - -**Protocol Buffers**: Uses Protocol Buffers for compact, efficient, and platform-neutral serialization of structured data. - -**HTTP/2**: Utilizes HTTP/2 for multiplexing, flow control, header compression, and efficient binary transport. - -**Bi-directional Streaming**: Supports multiple types of streaming, including client-side, server-side, and bi-directional streaming. - -**Cross-Language Compatibility**: Provides support for multiple backend programming languages, enabling interoperability between different systems. - -## Caching Systems - -![Caching Systems](https://assets.roadmap.sh/guest/working-cache-11kis.png) - -Caching systems store copies of frequently accessed data to reduce latency and improve application performance. They are essential for speeding up data retrieval and reducing the load on primary data stores. - -Implementing a successful caching strategy is not trivial, and one key aspect of it is the backend technology used for the implementation. While there might be multiple options out there, the industry currently recognizes only one de facto choice: Redis. - -### Redis: a fast in-memory storage solution - -Redis is an in-memory data structure store that can function as a database, cache, and message broker. It supports various data structures such as strings, hashes, lists, sets, sorted sets, bitmaps, hyperloglogs, and with the right add-ons, even vectors. Redis uses a key-value storage mechanism, which makes it simple yet powerful for a wide range of use cases. - -Let’s quickly review some of the characteristics of Redis that make it such a fantastic option. - -#### High Availability and Scalability - -- **Redis Sentinel**: Provides high availability and monitoring, automatically promoting a slave to master in case of failure, ensuring minimal downtime. - -- **Redis Cluster**: Supports automatic sharding, allowing Redis to scale horizontally. It partitions data across multiple nodes, ensuring that the system can handle large datasets and high throughput. - -#### Performance and Use Cases - -Redis's in-memory architecture gives it unmatched I/O speed, making it ideal for real-time applications such as: - -- **Gaming**: Managing leaderboards, player sessions, and real-time statistics. -- **Chat Applications**: Storing messages, user presence information, and delivering real-time notifications. -- **Analytics**: Real-time data processing and analytics, where rapid data access and manipulation are crucial. -- **Caching**: Reducing database load by caching frequently accessed data, improving application response times. - -#### Persistence and Durability - -- **RDB (Redis Database)**: Creates snapshots of the dataset at specified intervals, allowing data to be restored from the last snapshot. -- **AOF (Append Only File)**: Logs every write operation received by the server, providing a more durable solution that can replay the log to reconstruct the dataset. -- **Hybrid Approach**: Combining RDB and AOF to leverage the benefits of both methods, balancing performance and data durability. - -#### Advanced Features - -On top of all of that, Redis even provides some very powerful out-of-the-box features: - -- **Lua Scripting**: Supports server-side scripting with Lua, enabling complex operations to be executed atomically. -- **Pub/Sub Messaging**: Allows for message broadcasting to multiple clients, supporting real-time messaging and notifications. You can create whole event-based architectures around Redis. -- **Modules**: Extend Redis functionality with custom modules, such as RedisGraph for graph database capabilities and RedisJSON for JSON document storage. - -Redis's robust feature set, combined with its high performance and flexibility, makes it a versatile tool for developers looking to build scalable and responsive applications. - -## Message Brokers and Streaming Platforms - -![Message Brokers and Streaming Platforms](https://assets.roadmap.sh/guest/message-queue-yoq3q.png) - -Message brokers and streaming platforms facilitate communication between different parts of a system, enabling efficient data exchange and processing. They are crucial for building scalable and resilient applications and they are the core of reactive architectures (also known as event-based architectures). - -### RabbitMQ - -RabbitMQ is an open-source message broker that implements the Advanced Message Queuing Protocol (AMQP). It supports multiple messaging protocols and can be deployed in distributed and federated configurations. RabbitMQ is ideal for use cases that require reliable message delivery, complex routing, and interoperability with other messaging systems. It is commonly used in financial systems, order processing, and other applications that need robust messaging capabilities. - -### Apache Kafka - -Apache Kafka is a distributed streaming platform designed for high-throughput, low-latency data processing. It excels at handling real-time data feeds, making it suitable for applications that require continuous data integration and processing. Kafka’s publish-subscribe messaging system is fault-tolerant and scalable, making it ideal for big data applications, real-time analytics, event sourcing, and log aggregation. Its ability to store streams of records in a fault-tolerant manner also makes it useful for building event-driven architectures and microservices. - -As backend developers, understanding how to take advantage of these message queues is critical to the development of scalable and resilient platforms. It is definitely a must-have skill and you need to master it. - -## Authentication and Authorization - -![Authentication and Authorization](https://assets.roadmap.sh/guest/authentication-vs-authorization-vl6lg.png) - -Authentication and authorization technologies are essential for securing applications, ensuring that users are who they claim to be and have the appropriate permissions to access resources. - -This space is filled with solutions and methodologies, so it’s not easy to pick one option here, however, these two are very common solutions used to implement both, authZ (authorization) and authN (authentication). - -### OAuth - -OAuth is an open standard for access delegation commonly used to grant websites or applications limited access to a user’s information without exposing their passwords. It is widely used in single sign-on (SSO) systems, enabling users to log in to multiple applications with a single set of credentials. OAuth is ideal for third-party applications that need access to user data, such as social media integrations and API access management. - -### JWT (JSON Web Tokens) - -JWT is a compact, URL-safe means of representing claims to be transferred between two parties. The claims in a JWT are encoded as a JSON object, which is used as the payload of a JSON Web Signature (JWS) structure or as the plaintext of a JSON Web Encryption (JWE) structure. JWTs are commonly used for authentication and authorization in web applications, providing a secure way to transmit information between parties. They are particularly useful in stateless authentication systems, where user state is not stored on the server (like when dealing with RESTful APIs). - -## CI/CD Pipelines - -![CI/CD Pipelines](https://assets.roadmap.sh/guest/continous-development-vs-continuous-integration-rg7t9.png) - -CI/CD (Continuous Integration/Continuous Deployment) pipelines automate the process of code integration, testing, and deployment, enabling faster and more reliable software delivery. This is one of the key areas backend developers need to understand to avoid creating code that simply gets in the way of the deployment process. - -### GitHub Actions - -GitHub Actions is an integrated CI/CD service within GitHub repositories, allowing developers to automate build, test, and deployment workflows. It supports a wide range of actions and integrations, making it highly customizable and versatile for various development workflows. - -### CircleCI - -CircleCI is a continuous integration and delivery platform that automates the building, testing, and deployment of applications. It supports multiple backend languages and integrates with various version control systems, making it a popular choice for diverse development environments. CircleCI is known for its speed and ease of setup, providing robust tools for optimizing and monitoring CI/CD pipelines. - -### GitLab CI/CD - -GitLab CI/CD is an integrated part of the GitLab platform (similar to how GitHub actions are a part of GitHub), offering continuous integration, delivery, and deployment features within the GitLab ecosystem. It allows developers to manage their entire DevOps lifecycle in a single application, from planning and coding to monitoring and security. GitLab CI/CD is particularly useful for teams seeking a seamless and comprehensive CI/CD solution. - -### Jenkins - -If instead of a SaaS, you’re looking for a solution that you can potentially self-host, then you might want to look into Jenkins. Jenkins is an open-source automation server that provides hundreds of plugins to support building, deploying, and automating your software development process. It is highly extensible and can be integrated with a wide array of tools and technologies. Jenkins is ideal for complex, large-scale projects requiring a customizable and powerful CI/CD environment. - -## Monitoring and Logging - -![Monitoring and Logging](https://assets.roadmap.sh/guest/server-monitoring-vk5nb.png) - -Understanding how the systems that you develop behave and perform on a daily basis is crucial to launching a successful product. Here’s where monitoring and logging come into play. Monitoring and logging are crucial pieces of backend technology used for maintaining the health, performance, and security of applications. These tools help detect issues, analyze performance, and ensure system reliability. - -### ELK Stack (Elasticsearch, Logstash, Kibana) - -The ELK Stack is a set of tools for searching, analyzing, and visualizing log data in real time. Elasticsearch is a search and analytics engine, Logstash is a server-side data processing pipeline, and Kibana is a visualization tool. Together, they provide a powerful platform for centralized logging and monitoring, making them ideal for applications requiring detailed log analysis and real-time insights. - -### Grafana - -Grafana is an open-source platform for monitoring and observability that integrates with various data sources. It provides powerful visualizations, dashboards, and alerting capabilities, making it a popular choice for monitoring infrastructure and application performance. Grafana is particularly useful for teams needing a flexible and customizable monitoring solution. - -### Loki - -Loki is a log aggregation system designed to work with Grafana. It is optimized for cost-effective and scalable logging, making it suitable for applications with high log volumes. Loki simplifies log management by allowing developers to query logs using the same language as Grafana, providing seamless integration for comprehensive observability. - -### Prometheus - -Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. It collects and stores metrics as time series data, providing powerful querying language and alerting capabilities. Prometheus is ideal for monitoring applications and infrastructure, particularly in cloud-native and microservices environments, where dynamic and ephemeral resources are common. - -In the end, you might want to go with one or several of these options, the point is that you, as a developer, should be aware of them and what type of value they add to the project. - -## Conclusion - -As backend developers, focusing on a backend programming language and a backend framework is not going to be enough. The backend ecosystem is very rich, and there are many areas that are either directly or indirectly related to the daily tasks that a backend dev needs to work on. - -This is why you need to stay up-to-date and look at the trends that develop within each area to make sure you’re still working with and focusing on the right solutions. - -If you'd like more details on the type of backend development technologies you should be focusing on to excel at your role as a backend developer, check out our [Backend Developer Roadmap](https://roadmap.sh/backend). diff --git a/src/data/guides/basic-authentication.md b/src/data/guides/basic-authentication.md deleted file mode 100644 index 9b5fd3980..000000000 --- a/src/data/guides/basic-authentication.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: 'Basic Authentication' -description: 'Understand what is basic authentication and how it is implemented' -authorId: 'kamran' -seo: - title: 'Basic Authentication - roadmap.sh' - description: 'Understand what is basic authentication and how it is implemented' -isNew: false -type: 'visual' -date: 2021-05-19 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'visual-guide' - - 'guide-sitemap' ---- - -[![Basic Authentication](/guides/basic-authentication.png)](/guides/basic-authentication.png) diff --git a/src/data/guides/basics-of-authentication.md b/src/data/guides/basics-of-authentication.md deleted file mode 100644 index 2fd1aa617..000000000 --- a/src/data/guides/basics-of-authentication.md +++ /dev/null @@ -1,103 +0,0 @@ ---- -title: 'Basics of Authentication' -description: 'Learn the basics of Authentication and Authorization' -authorId: 'kamran' -seo: - title: 'Basics of Authentication - roadmap.sh' - description: 'Learn the basics of Authentication and Authorization' -isNew: false -type: 'textual' -date: 2022-09-21 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -Our last video series was about data structures. We looked at the most common data structures, their use cases, pros and cons, and the different operations you could perform on each data structure. - -Today, we are kicking off a similar series for Authentication strategies where we will discuss everything you need to know about authentication and authentication strategies. - -In this guide today will be talking about what authentication is, and we will cover some terminology that will help us later in the series. You can watch the video below or continue reading this guide. - - - -## What is Authentication? - -Authentication is the process of verifying someone's identity. A real-world example of that would be when you board a plane, the airline worker checks your passport to verify your identity, so the airport worker authenticates you. - -If we talk about computers, when you log in to any website, you usually authenticate yourself by entering your username and password, which is then checked by the website to ensure that you are who you claim to be. There are two things you should keep in mind: - -- Authentication is not only for the persons -- And username and password are not the only way to authenticate. - -Some other examples are: - -- When you open a website in the browser. If the website uses HTTP, TLS is used to authenticate the server and avoid the fake loading of websites. - -- There might be server-to-server communication on the website. The server may need to authenticate the incoming request to avoid malicious usage. - -## How does Authentication Work? - -On a high level, we have the following factors used for authentication. - -- **Username and Password** -- **Security Codes, Pin Codes, or Security Questions** — An example would be the pin code you enter at an ATM to withdraw cash. -- **Hard Tokens and Soft Tokens** — Hard tokens are the special hardware devices that you attach to your device to authenticate yourself. Soft tokens, unlike hard tokens, don't have any authentication-specific device; we must verify the possession of a device that was used to set up the identity. For example, you may receive an OTP to log in to your account on a website. -- **Biometric Authentication** — In biometric authentication, we authenticate using biometrics such as iris, facial, or voice recognition. - -We can categorize the factors above into three different types. - -- Username / Password and Security codes rely on the person's knowledge: we can group them under the **Knowledge Factor**. - -- In hard and soft tokens, we authenticate by checking the possession of hardware, so this would be a **Possession Factor**. - -- And in biometrics, we test the person's inherent qualities, i.e., iris, face, or voice, so this would be a **Qualities** factor. - -This brings us to our next topic: Multi-factor Authentication and Two-Factor Authentication. - -## Multifactor Authentication - -Multifactor authentication is the type of authentication in which we rely on more than one factor to authenticate a user. - -For example, if we pick up username/password from the **knowledge factor**. And we pick soft tokens from the **possession factor**, and we say that for a user to authenticate, they must enter their credentials and an OTP, which will be sent to their mobile phone, so this would be an example of multifactor authentication. - -In multifactor authentication, since we rely on more than one factor, this way of authentication is much more secure than single-factor authentication. - -One important thing to note here is that the factors you pick for authentication, they must differ. So, for example, if we pick up a username/password and security question or security codes, it is still not true multifactor authentication because we still rely on the knowledge factor. The factors have to be different from each other. - -### Two-Factor Authentication - -Two-factor authentication is similar to multifactor authentication. The only difference is that there are precisely two factors in 2FA. In MFA, we can have 2, 3, 4, or any authentication factors; 2FA has exactly two factors. We can say that 2FA is always MFA, because there are more than one factors. MFA is not always 2FA because there may be more than two factors involved. - -Next we have the difference between authentication and authorization. This comes up a lot in the interviews, and beginners often confuse them. - -### What is Authentication - -Authentication is the process of verifying the identity. For example, when you enter your credentials at a login screen, the application here identifies you through your credentials. So this is what the authentication is, the process of verifying the identity. - -In case of an authentication failure, for example, if you enter an invalid username and password, the HTTP response code is "Unauthorized" 401. - -### What is Authorization - -Authorization is the process of checking permission. Once the user has logged in, i.e., the user has been authenticated, the process of reviewing the permission to see if the user can perform the relevant operation or not is called authorization. - -And in case of authorization failure, i.e., if the user tries to perform an operation they are not allowed to perform, the HTTP response code is forbidden 403. - -## Authentication Strategies - -Given below is the list of common authentication strategies: - -- Basic Authentication -- Session Based Authentication -- Token-Based Authentication -- JWT Authentication -- OAuth - Open Authorization -- Single Sign On (SSO) - -In this series of illustrated videos and textual guides, we will be going through each of the strategies discussing what they are, how they are implemented, the pros and cons and so on. - -So stay tuned, and I will see you in the next one. diff --git a/src/data/guides/big-o-notation.md b/src/data/guides/big-o-notation.md deleted file mode 100644 index 8eb6c8ae7..000000000 --- a/src/data/guides/big-o-notation.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -title: 'Big-O Notation' -description: 'Easy to understand explanation of Big-O notation without any fancy terms' -authorId: 'kamran' -seo: - title: 'Big-O Notation - roadmap.sh' - description: 'Easy to understand explanation of Big-O notation without any fancy terms' -isNew: false -type: 'visual' -date: 2021-03-15 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'visual-guide' - - 'guide-sitemap' ---- - -Big-O notation is the mathematical notation that helps analyse the algorithms to get an idea about how they might perform as the input grows. The image below explains Big-O in a simple way without using any fancy terminology. - -[![Big-O Notation](/guides/big-o-notation.png)](/guides/big-o-notation.png) diff --git a/src/data/guides/character-encodings.md b/src/data/guides/character-encodings.md deleted file mode 100644 index 268390d2d..000000000 --- a/src/data/guides/character-encodings.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: 'Character Encodings' -description: 'Covers the basics of character encodings and explains ASCII vs Unicode' -authorId: 'kamran' -seo: - title: 'Character Encodings - roadmap.sh' - description: 'Covers the basics of character encodings and explains ASCII vs Unicode' -isNew: false -type: 'visual' -date: 2021-05-14 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'visual-guide' - - 'guide-sitemap' ---- - -[![Character Encodings](/guides/character-encodings.png)](/guides/character-encodings.png) diff --git a/src/data/guides/ci-cd.md b/src/data/guides/ci-cd.md deleted file mode 100644 index 135c62b69..000000000 --- a/src/data/guides/ci-cd.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -title: 'What is CI and CD?' -description: 'Learn the basics of CI/CD and how to implement that with GitHub Actions.' -authorId: 'kamran' -seo: - title: 'What is CI and CD? - roadmap.sh' - description: 'Learn the basics of CI/CD and how to implement that with GitHub Actions.' -isNew: false -type: 'visual' -date: 2021-07-09 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'visual-guide' - - 'guide-sitemap' ---- - -The image below details the differences between the continuous integration and continuous delivery. Also, here is the [accompanying video on implementing that with GitHub actions](https://www.youtube.com/watch?v=nyKZTKQS_EQ). - -[![CI vs CD](/guides/ci-cd.png)](/guides/ci-cd.png) diff --git a/src/data/guides/consistency-patterns-in-distributed-systems.md b/src/data/guides/consistency-patterns-in-distributed-systems.md deleted file mode 100644 index 821879133..000000000 --- a/src/data/guides/consistency-patterns-in-distributed-systems.md +++ /dev/null @@ -1,72 +0,0 @@ ---- -title: 'Consistency Patterns' -description: 'Everything you need to know about Week, Strong and Eventual Consistency' -authorId: 'kamran' -seo: - title: 'Consistency Patterns - roadmap.sh' - description: 'Everything you need to know about Week, Strong and Eventual Consistency' -isNew: false -type: 'textual' -date: 2023-01-18 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'visual-guide' - - 'guide-sitemap' ---- - -Before we talk about the Consistency Patterns, we should know what a distributed system is. Simply put, a distributed system is a system that consists of more than one components, and each component is responsible for one part of the application. - -> A distributed system is a system whose components are located on different networked computers, which communicate and coordinate their actions by passing messages to one another. The components interact with one another in order to achieve a common goal. - Wikipedia - -## Distributed Systems - -Imagine we have an e-commerce application where we are selling books. This application may consist of multiple different components. For example, one server might be responsible for the accounts, another might be responsible for the payments, one might be responsible for storing orders, one might be responsible for loyalty points and relevant functionalities, and another might be responsible for maintaining the books inventory and so on. - -![Book Store - Distributed System](https://i.imgur.com/VHATt7a.png) - -Now, if a user buys a book, there might be different services involved in placing the order; order service for storing the order, payment service for handling the payments, and inventory service for keeping the stock of that ordered book up to date. This is an example of a distributed system, an application that consists of multiple different components, each of which is responsible for a different part of the application. - -## Why is Consistency Important? - -When working with distributed systems, we need to think about managing the data across different servers. If we take the above example of the e-commerce application, we can see that the inventory service must have up-to-date stock information for the ordered items if the user places an order. Now, there might be two different users looking at the same book. Now imagine if one of the customers places a successful order, and before the inventory service can update the stock, the second customer also places the order for the same book. In that case, when the inventory wasn't updated, we will have the wrong stock information when the second order was placed, i.e., the ordered book may or may not be available in stock. This is where different consistency patterns come into play. They help ensure that the data is consistent across the application. - -## Consistency Patterns - -Consistency patterns refer to the ways in which data is stored and managed in a distributed system and how that data is made available to users and applications. There are three main types of consistency patterns: - -- Strong consistency -- Weak consistency -- Eventual Consistency - -Each of these patterns has its own advantages and disadvantages, and the choice of which pattern to use will depend on the specific requirements of the application or system. - -### Strong Consistency - -> After an update is made to the data, it will be immediately visible to any subsequent read operations. The data is replicated in a synchronous manner, ensuring that all copies of the data are updated at the same time. - -In a strong consistency system, any updates to some data are immediately propagated to all locations. This ensures that all locations have the same version of the data, but it also means that the system is not highly available and has high latency. - -An example of strong consistency is a financial system where users can transfer money between accounts. The system is designed for **high data integrity**, so the data is stored in a single location and updates to that data are immediately propagated to all other locations. This ensures that all users and applications are working with the same, accurate data. For instance, when a user initiates a transfer of funds from one account to another, the system immediately updates the balance of both accounts and all other system components are immediately aware of the change. This ensures that all users can see the updated balance of both accounts and prevents any discrepancies. - -### Weak Consistency - -> After an update is made to the data, it is not guaranteed that any subsequent read operation will immediately reflect the changes made. The read **may or may not** see the recent write. - -In a weakly consistent system, updates to the data may not be immediately propagated. This can lead to inconsistencies and conflicts between different versions of the data, but it also allows for **high availability and low latency**. - -Another example of weak consistency is a gaming platform where users can play online multiplayer games. When a user plays a game, their actions are immediately visible to other players in the same data center, but if there was a lag or temporary connection loss, the actions may not be seen by some of the users and the game will continue. This can lead to inconsistencies between different versions of the game state, but it also allows for a high level of availability and low latency. - -### Eventual Consistency - -> Eventual consistency is a form of Weak Consistency. After an update is made to the data, it will be eventually visible to any subsequent read operations. The data is replicated in an asynchronous manner, ensuring that all copies of the data are eventually updated. - -In an eventually consistent system, data is typically stored in multiple locations, and updates to that data are eventually propagated to all locations. This means that the system is highly available and has low latency, but it also means that there may be inconsistencies and conflicts between different versions of the data. - -An example of eventual consistency is a social media platform where users can post updates, comments, and messages. The platform is designed for high availability and low latency, so the data is stored in multiple data centers around the world. When a user posts an update, the update is immediately visible to other users in the same data center, but it may take some time for the update to propagate to other data centers. This means that some users may see the update while others may not, depending on which data center they are connected to. This can lead to inconsistencies between different versions of the data, but it also allows for a high level of availability and low latency. - -## Conclusion - -In conclusion, consistency patterns play a crucial role in distributed systems, and the choice of which pattern to use will depend on the specific requirements of the application or system. Each pattern has its own advantages and disadvantages, and each is more suitable for different use cases. Weak consistency is suitable for systems that require high availability and low latency, strong consistency is suitable for systems that require high data integrity, and eventual consistency is suitable for systems that require both high availability and high data integrity. diff --git a/src/data/guides/data-analyst-career-path.md b/src/data/guides/data-analyst-career-path.md deleted file mode 100644 index 2e75f6758..000000000 --- a/src/data/guides/data-analyst-career-path.md +++ /dev/null @@ -1,147 +0,0 @@ ---- -title: 'Data Analyst Career Path: My Pro Advice' -description: 'Wondering where a data analyst role can take you? Learn what your data analyst career path could look like.' -authorId: fernando -excludedBySlug: '/data-analyst/career-path' -seo: - title: 'Data Analyst Career Path: My Pro Advice' - description: 'Wondering where a data analyst role can take you? Learn what your data analyst career path could look like.' - ogImageUrl: 'https://assets.roadmap.sh/guest/data-analyst-career-path-heu4b.jpg' -isNew: false -type: 'textual' -date: 2025-05-15 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Choosing your data analyst career path](https://assets.roadmap.sh/guest/data-analyst-career-path-heu4b.jpg) - -# Data Analyst Career Path: My Pro Advice - -Data analysts sit at the heart of decision‑making in virtually every industry today. From uncovering customer behavior patterns in retail to optimizing operations in healthcare, the ability to collect, clean, and interpret data has become a critical superpower. - -To help you map out your future data analyst career path, in this guide I'll mix in two cornerstone resources from roadmap.sh: the very detailed [Data Analyst Roadmap](https://roadmap.sh/data-analyst), which lays out the skills and milestones you'll need from beginner to pro, and the hands‑on [SQL Course](https://roadmap.sh/courses/sql), designed to build your foundation in one of the most common languages when it comes to data operations. Together, these tools will serve as your compass and toolkit, ensuring you have a clear path forward and the practical know‑how to tackle real‑world challenges. - -## Options for a Data Analyst Career Path - -![Options for a Data Analyst Career Path](https://assets.roadmap.sh/guest/data-analyst-career-options-ct705.png) - -Knowing where to go and how to grow in data analysis is not trivial, simply because there are too many very valid and interesting options for data analysts. - -To narrow the list of options, we can think of three core trajectories, each with its own set of responsibilities, key skills, and growth opportunities: - -### Junior Data Analyst → Senior Data Analyst → Analytics Manager - -| Level | Focus | Key skills | Goal | -| :---- | :---- | :---- | :---- | -| **Entry-level (i.e junior analyst)** | Clean & transform data using SQL or MS Excel | Basic data modeling, reporting, req. gathering. | Provide actionable insights. | -| **Mid-level** | Complex analysis, advanced statistical analysis, and project ownership. | Python/R, creating ETLs, mentoring | Shaping data strategy, collaborating with business or operations, and influencing decision-making | -| **Leadership** | Defining implementation roadmaps | Leadership, stakeholder management, expectation management with clients. | Lead key meetings with clients, become VP of analytics, or similar role. | - -### Data Analytics Consultant / BI Analyst - -| Level | Focus | Key Skills | Goal | -| :---- | :---- | :---- | :---- | -| **Data Analytics Consultant** | Acting as a strategic advisor, you help organizations define their data strategy and translate business requirements. | Data Strategy & Governance, Client Engagement, deep SQL. | Delivering a scalable analytics roadmap, implementing dashboards, and earning trust as a go‑to advisor. | -| **BI Analyst** | Embedding within a single organization or business unit to build and maintain self‑service reporting environments. | ETL, dashboard development. | Influence data strategy, mentor JR Data Scientists. | - -### Specialized Data Scientist Tracks → Chief Data Officer - -| Level | Focus | Key Skills | Goal | -| :---- | :---- | :---- | :---- | -| **Data science option** | Go from descriptive analytics to machine learning algorithms. | Advanced Python, Statistical Analysis, Data modeling. | Deliver a working predictive solution | -| **Advanced Statistics** | Tackle large‑scale analytical problems | Expertise in advanced statistical programming, big Data, and a bit of storytelling | Influence data strategy, mentor JR Data Scientists | -| **CDO** | Oversee data governance, compliance, and ensure that analytics and machine learning initiatives align with strategic objectives. | Strategic leadership, understanding of data privacy, data governance. | Implement robust data governance & privacy frameworks, deliver analytics roadmap. | - -### What should you pick? - -In the end, either through any of these variations of the data analyst career path, there isn't a single option that is clearly better than the others. - -* If you love turning raw numbers into charts and dashboards, the **junior→senior analyst** route offers steady, skill‑based progression. - -* If you thrive on variety and advising multiple teams, consider the **analytics consultant/BI analyst** track. - -* If you're drawn to algorithms and predictive work, the **data science** trajectory can propel you toward senior data scientist roles and, ultimately, a chief data officer position. - -## Is Data Analysis Right for You? - -Figuring out if the data analyst career path is the right place for you is not an easy task; in fact, many will need to go through the process of working in the field to retroactively answer the question. - -But to give you a basic guide and help you understand whether you'd enjoy the position or not, you have to consider that pursuing a data analytics career begins with an honest curiosity about how raw data translates into actionable insights. Data analysis isn't just number crunching; it's about asking the right questions, designing robust statistical tests, and building data models that answer real business problems. - -## Learning Path & Essential Skills - -Charting your learning path starts with a clear learning roadmap, and there's no better place to begin than the [Data Analyst Roadmap](https://roadmap.sh/data-analyst). - -![Learning Path & Essential Skills](https://assets.roadmap.sh/guest/learning-path-essential-skills-k9zy3.png) - -Following its structured progression ensures you're building the right technical skill set in the right order. - -As part of the roadmap, you'll have to tackle different languages such as SQL, R, Python, and others. To learn more about it, you can try this hands-on [SQL Course](https://roadmap.sh/courses/sql) that walks you through writing efficient queries, designing relational schemas, and performing complex joins and aggregations. - -You'll also need **data visualization tools** and the storytelling mindset that makes your analyses resonate. - -Finally, you'll start noticing that soft skills are particularly needed as a data analyst. For example, clear communication, problem solving, and a collaborative spirit are non‑negotiable when gathering requirements, iterating on dashboards, or presenting to senior management. - -## 3 Portfolio Project Ideas - -Below are three end‑to‑end projects designed to showcase the abilities that hiring managers look for in data analyst candidates. Each idea maps to stages on the [Data Analyst Roadmap](https://roadmap.sh/data-analyst) and gives you a chance to apply SQL, Python/R, and visualization tools to real‑world questions. - -### Interactive Sales Dashboard - -**Objective:** In this project, you can build a live dashboard that empowers marketing and senior management to spot seasonal patterns, best‑selling products, and under‑performing regions. - -**Data & tools:** For this project, you can source a public retail or e-commerce dataset (such as Kaggle "Online Retail II"). You can use Python and SQL, the rest is up to you to decide how to show the results. - -**Key skills demonstrated:** In this project, you're covering a bit of Data Modeling, ETL pipelines, and mostly Data Visualization tools. - -### Customer Churn Prediction Model - -**Objective:** For this one, you'll show how statistical analysis and basic machine learning can predict which customers are most likely to churn, enabling proactive retention strategies. - -**Data & Tools:** For this one, you can find some sort of telecom dataset (like IBM Telco Customer Churn), use Python and SQL again to do some exploratory analysis, and finally train a classification model using scikit-learn. - -**Key skills demonstrated:** During this project, you'll work on statistical analysis, data mining, and, as usual, some actionable insights turned into storytelling. - -### A/B Testing Analysis for Website Redesign - -**Objective**: Conduct and interpret an A/B test to determine which landing‑page design maximizes conversion, showcasing your ability to drive business analytics projects from hypothesis to recommendation. - -**Data & Tools**: You can get some synthetic data for this one using something like ChatGPT, as long as it simulates A/B test data. Then, using either SQL or even MS Excel, you can do some aggregations and finally do the last calculations with Python or R. Try to plot the results on something like PowerBI at the end. - -**Key skills demonstrated**: For this project, you'll be doing some experimental design, some business intelligence, and of course, decision making by translating statistical outcomes into a go/no‑go recommendation, acting as a market research analyst. - -![Data Analyst Portfolio Projects](https://assets.roadmap.sh/guest/data-analyst-portfolio-projects-tjm2y.png) - -## My tips from personal experience - -With all of this out of the way, let me quickly run you through some of my personal tips when it comes to growing and moving forward as a data analyst. - -1. **Build a strong network and find mentors:** Connect with other data analysts, data scientists, and analytics managers through LinkedIn groups, local meetups, or virtual conferences. Ask others who have gone through the same about their journey, about the problems they found along the way. Learn from them. -2. **Showcase your work with purpose:** Your first data analyst job will depend on having a solid portfolio (since you don't have any actual experience). Try to host your projects on GitHub or a personal blog, and include clear READMEs that explain your data strategy, the tech stack you used, and the business impact (showing you understand the value of your work), whether it's "increased conversion rate by X%" or "optimized inventory planning". -3. **Stay ahead with the latest tools and techniques:** Data visualization tools and programming languages are constantly evolving. One key language you'll be using quite regularly is SQL, and if you ignore it, your progress will slow down. Find yourself a [SQL Course](https://roadmap.sh/courses/sql) that works for you and ensure you master it as soon as possible. -4. **Embrace feedback and cultivate a growth mindset:** Whether you're presenting to marketing teams or senior management, feedback is your friend. After each project or presentation, or even on a regular basis try to get constructive feedback on your data modeling, storytelling, and communication style. Use this input to refine your processes, improving both your essential skills and your ability to communicate insights. -5. **Plan for credentials that matter:** Getting credentials that validate your expertise with a certain tool or a type of analysis is going to help you stand out in the sea of analysts fighting for the same position. So, consider pursuing data analytics certifications (e.g., Google Data Analytics or Microsoft Power BI). They will not ensure you get the job, but they'll help you demonstrate a certain level of expertise at first glance. - -## Conclusion - -Congrats, you now have a clear playbook for launching and advancing your data analyst career: - -1. **Choose your path.** Understand exactly what you enjoy the most, and find the best career path for you. - -2. **Assess your fit**. Understand the role you want, and make sure you'll enjoy the day-to-day of it. - -3. **Build your skills**. Follow the [Data Analyst Roadmap](https://roadmap.sh/data-analyst) to structure your learning, and dive into the [SQL Course](https://roadmap.sh/courses/sql) to master the foundation of every data role. - -4. **Practice with real projects**. Even if it's with fake, test or even raw data, tackle real-world problems to show you're able to transmit insights in the right way. - -5. **Finally,** remember to network with other analysts, seek feedback, stay current on tools and techniques, and earn targeted certifications when you're ready to stand out. - -Your journey into becoming a successful data analyst begins today: pick one section of the roadmap, schedule time to complete the SQL course module, and start your first portfolio project. - -Go! \ No newline at end of file diff --git a/src/data/guides/data-analyst-how-to-become.md b/src/data/guides/data-analyst-how-to-become.md deleted file mode 100644 index 1abbd74df..000000000 --- a/src/data/guides/data-analyst-how-to-become.md +++ /dev/null @@ -1,218 +0,0 @@ ---- -title: "How to Become a Data Analyst with No Experience: My Advice" -description: "Learn how to become a data analyst with no experience through smart steps, skill-building tips, and my real-world guidance." -authorId: fernando -excludedBySlug: '/data-analyst/how-to-become' -seo: - title: "How to Become a Data Analyst with No Experience: My Advice" - description: "Learn how to become a data analyst with no experience through smart steps, skill-building tips, and my real-world guidance." - ogImageUrl: 'https://assets.roadmap.sh/guest/become-a-data-analyst-with-no-experience-khk03.jpg' -isNew: true -relatedTitle: "Other Guides" -relatedGuidesId: data-analyst -type: 'textual' -date: 2025-05-14 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![How to Become a Data Analyst with No Experience](https://assets.roadmap.sh/guest/become-a-data-analyst-with-no-experience-khk03.jpg) - -Breaking into the data analytics industry can be scary, especially when you have no prior background in data analysis. - -Yet the need for entry-level data analysts has never been higher, data is at the heart of everything, and organizations across the globe depend on data-driven insights to inform strategic decisions in every area of the enterprise. - -Many aspiring data professionals incorrectly believe that a degree in computer science or a long resume in data science is a prerequisite for anything. The truth is that mastering the **data analysis process**, building a solid **analytics portfolio**, and showcasing your **data skills** can open doors to your first **junior data analyst** role, just like a long resume or a fancy degree. - -In this practical guide on how to become a data analyst with no experience, I'm going to show you the key skills you should focus your learning path on (like SQL and statistical analysis). - -I'll also prepare you on how to hone critical soft skills and leverage expert resources such as the [Data Analyst Roadmap](https://roadmap.sh/data-analyst) and this [SQL course](https://roadmap.sh/courses/sql) to help you improve your skills in that language. - -## Step 1: Master the Essential Data Analyst Skills - -In the analysis life cycle (from data collection and unprocessed data wrangling to statistical modeling and presentation), some **techniques** and **skills (both technical and non-technical)** are non-negotiable. The first thing you have to do is to focus on these core areas to build a solid foundation for your first **entry-level data analyst** or **junior data analyst** role: - -### Technical Skills: - -A data analyst's toolkit revolves around five foundational technical domains, each critical to unlocking insights from raw data and driving informed decisions. Begin by mastering **Structured Query Language (SQL)**, the backbone of relational data interrogation. With our [**SQL course on roadmap.sh**](https://roadmap.sh/courses/sql), you will learn to write complex queries—filtering, joining, aggregations, and advanced window functions—that enable you to extract and **manipulate data** at scale. - -Next, tackle Data cleansing and Manipulation, the art of transforming messy inputs into reliable analytical datasets. Utilizing libraries such as pandas in Python or dplyr in R, you will develop workflows for handling missing values, normalizing formats, and removing duplicates, thereby ensuring high **data quality** before any modeling or visualization begins. - -With a pristine dataset in hand, dive into different Analysis techniques to derive meaningful patterns. Key concepts such as descriptive statistics, hypothesis testing, correlations, and regression form the core of **these analysis techniques** used to validate assumptions and generate **actionable insights**. Proficiency in these methods equips you to support business cases with evidence-backed conclusions. - -Translating numerical results into compelling narratives requires strong **Data Visualization** skills. Whether you choose Tableau, Power BI, or Python's matplotlib and seaborn libraries, you will create interactive dashboards and visual stories that highlight trends, outliers, and opportunities. Effective visualization not only conveys findings clearly but also drives stakeholder engagement. - -Finally, solid programming skills in Python or R tie your analytical process together. From automating repetitive tasks and integrating APIs to building end-to-end **data analytics** pipelines, a programming mindset enables scalability, reproducibility, and integration of advanced tools, positioning you for roles across the industry. - -### Soft Skills: - -In the realm of data analytics, technical prowess must be complemented by strong interpersonal abilities (i.e, soft skills). Analytical thinking and structured problem-solving are at the heart of your daily workflow. When you encounter complex datasets, you must decide which analysis techniques best uncover the story hidden in the numbers. - -This thoughtful approach ensures you derive accurate, actionable insights from unprocessed data. - -Equally vital is the capacity to interpret data and translate it into meaningful recommendations. Effective communication means crafting a narrative around your findings that resonates with diverse audiences, from C-suite executives to frontline team members. - -By designing clear visualizations and presenting data insights through compelling dashboards or concise slide decks, you empower stakeholders to make informed decisions. - -Documenting every phase of your analysis process (data collection, cleaning, transformation, modeling, and reporting) creates a transparent audit trail. This meticulous record-keeping not only bolsters confidence in your results but also sets the foundation for reproducibility and continuous improvement. - -Collaboration and adaptability define success in dynamic business environments. As a business intelligence analyst, sales data analyst, or healthcare data analyst, you'll partner with professionals across marketing, finance, operations, and IT. - -Learning to navigate different communication styles, incorporating feedback, and swiftly adopting new tools or programming skills are essential to deliver timely, value-driven analyses. - -Finally, incorporate a growth mindset by viewing every project as an opportunity to refine your soft skills. Seek constructive feedback from mentors and peers, participate in cross-functional workshops, and mentor aspiring professionals. Cultivating empathy, resilience, and lifelong learning habits ensures you evolve into a well-rounded data analyst capable of driving organizational success. - -Starting with these essential skills will give you the confidence to tackle **data-projects**, close the **data skills gap**, and stand out among **aspiring analysts** in a competitive **job market.** After the foundation is done, you're free to specialize and focus in the areas that you feel are more appealing to you. - -## Step 2: Follow a Structured Learning Path - -Getting started on your journey as a new data analyst without formal experience can feel a bit overwhelming, so it's very important to follow a **structured learning path**. This way, you'll ensure you acquire the right **data analysis skills** in a way that makes sense according to your progression during your journey. - -Here's how to follow a proven roadmap: - -1. **Explore the [Data Analyst Roadmap](https://roadmap.sh/data-analyst)**: Start with the roadmap to grasp the full analysis life cycle. -2. **Master SQL Early**: As a key technology for the field, the sooner you start tackling SQL, the better it will be. So, enroll in a [SQL course](https://roadmap.sh/courses/sql) to build a strong foundation in SQL (Structured Query Language). Practice querying, filtering, aggregations, and manipulation on realistic datasets. -3. **Earn Recognized Certifications**: Boost your resume with entry-level certifications that validate your expertise. Consider doing any of the following: - * **Google Data Analytics Professional Certificate** (Coursera) - * **IBM Data Analyst Professional Certificate** - * **Microsoft Certified: Data Analyst Associate**. These programs cover essential **data science** concepts, **data cleansing**, **visualization**, and real-world **data projects**. -4. **Take Complementary Courses**: Figure out what your **data analysis skills gaps** are and fill them up by learning: - * **Programming languages** (Python, R) - * **Statistical analysis** and hypothesis testing - * **Business intelligence** tools (Tableau, Power BI). Platforms like DataCamp, Udacity, and LinkedIn Learning offer targeted modules. -5. **Apply Knowledge Through Projects**: Especially at the beginning, when you don't have any experience, try to reinforce your learning by tackling guided analysis projects on platforms like [Kaggle](https://www.kaggle.com/) or GitHub. Focus on end-to-end workflows (data collection, data cleaning, analyzing data, visualizing data, and presenting insights). - -This structured learning path will help you in several ways: - -* You'll be able to systematically build basic data analysis skills. -* You'll end up developing a compelling and very complete portfolio showcasing your skills. -* And you'll also be able to demonstrate to potential hiring managers your commitment to becoming a great junior data analyst. - -## Step 3: Build a Data Analytics Portfolio - -Building a robust data analyst portfolio will help you demonstrate your ability to tackle end-to-end **projects**, making you a standout candidate in a competitive **job market**. Here's why it matters and how to craft compelling portfolio pieces: - -### Why a Strong Portfolio Matters - -Building a strong portfolio helps you validate your own **analysis process**, from **data collection** and cleaning to visualization and interpretation, you'll be working on the entire thing. - -It also helps you showcase your proficiency in **data manipulation & cleaning**, and extracting **actionable insights** from unprocessed data. - -As part of the analysis life cycle, your **data visualization** and storytelling skills will have to shine in your portfolio. They're critical for roles like **BI analyst** or **data analyst**, so make sure you use them on every project in your portfolio. - -Overall, the portfolio will help you highlight every single skill that is needed for the job, and if you build one with varied projects, different types of visualizations, and business goals, by the end, you'll be broadcasting to every hiring manager that you know what you're doing. - -### Portfolio Project Ideas - -But what project can you put into your portfolio? Let's go over some ideas that might highlight just the right set of skills: - -* **Public Dataset Analysis**: Pick datasets (e.g., healthcare metrics, sales transactions, fraud detection) and perform a full workflow—ingestion, cleaning, exploratory analysis, statistical modeling, and reporting. -* **Data Cleaning & QA Showcase**: Use a messy real-world dataset to demonstrate handling missing values, outliers, normalization, and quality checks—include before/after snapshots and code snippets. -* **Interactive Dashboards**: Build dashboards with Tableau, Power BI, or Plotly to **visualize data** trends and present insights; add filters, annotations, and user controls. -* **Domain-Specific Projects**: Create analyses for niche roles—e.g., evaluate patient outcomes as a **healthcare data analyst**. -* **Data Storytelling**: Craft a narrative-driven project (e.g., COVID-19 trends, climate data) combining charts, maps, and written insights to tell a compelling story. - -And if you don't really know how to begin working on them, you can simply ask one of the many LLMs available online to give you a detailed project plan, and start working on it. You'll be closing projects right and left in no time. - -### Best Practices - -Where should you create your portfolio? What tech stack should you use? How often should you update it? When should you publish it? -These are all valid questions that don't have a single answer, however, I can show you some of the best practices I've seen around throughout my career when it comes to building data analyst portfolios: - -* Host projects on GitHub with clear READMEs, organized code, and visual previews. -* Document your **analysis life cycle** in Jupyter notebooks or blog posts, explaining each step and decision. -* Use authentic tools and workflows: query public APIs with **SQL**, automate tasks with Python or R, and integrate BI tools. -* Continuously update your portfolio as you learn new **data analysis techniques** and **programming skills**. - -## Step 4: Gain Practical Experience - -Securing practical experience allows you to apply theoretical knowledge, bridge the skills gap, and prove to hiring managers that you can deliver on real-world data analysis projects. Here are several pathways to get your foot in the door: - -* **Internships & Volunteer Projects:** - Look for data internships (even unpaid if you can) or volunteer to help non‑profits, student organizations, or local businesses with their **data collection**, **data cleaning**, and **data visualization** needs. These roles not only strengthen your **technical skills** but also give you concrete examples for your **data analytics portfolio**. -* **Freelance & Gig Work:** - Platforms like Upwork, Fiverr, or even LinkedIn can connect you with short‑term gigs, everything from database queries to dashboard creation. Freelance tasks force you to **manipulate data**, produce **actionable insights**, and **present data-driven insights** under real-world deadlines, building both technical chops and business communication skills. -* **Entry‑Level & Support Roles:** - Apply for roles labeled **data technician**, **data entry**, or **junior data analyst**. These positions often focus on **data quality**, routine reports, and simple analyses; they're perfect stepping stones that let you collaborate with senior analysts and refine your **analysis** and **manipulation** techniques. -* **Kaggle Competitions & Public Challenges:** - I've mentioned this platform before, but consider participating in Kaggle or similar platforms to demonstrate your ability to tackle problems like a **fraud detection** challenge or sales forecasting. Even if you don't win, documenting your approach, code, and learnings shows resilience and a methodical **analysis process**. -* **Capstone & Guided Projects:** - Many certification programs include capstone projects. Treat these as mini‑portfolio pieces: choose datasets relevant to your target roles, and clearly outline your **data analysis life cycle**, from **raw data** ingestion through **data visualization** and storytelling. - -Build experience in as many diverse **practical experiences as you can**. That way, you'll not only close any **data skills gap** you might have, but also amass a collection of tangible accomplishments, making it far easier to articulate your value as an **aspiring data analyst** during interviews. - -## Step 5: Network and Engage with the Data Community - -Spending time in the right communities accelerates your growth as an **aspiring data analyst** by helping you stay current with industry trends, learn from seasoned **professionals**, and uncover hidden **data analytics job** opportunities. After all, a big part of growing in any technology role is about networking (shocking, I know\!) with other professionals who've gone through what you're going through now and can provide advice, help, or even a boost when least expected. - -Networking is not easy, and there are many ways to do it. Here's just one example of how to build and leverage your own network: - -* **Leverage Professional Platforms:** Connect with **qualified data analysts** and **data scientists** on LinkedIn, GitHub, and specialized forums like Stack Overflow or Reddit's r/datascience. Engage by asking thoughtful questions, sharing your **data analysis projects**, and commenting on discussions. -* **Attend Industry Events:** Participate in local meetups, virtual conferences, webinars, and workshops hosted by organizations such as Data Science Saudi, Meetup groups, or platform-specific events (e.g., Tableau User Groups). These gatherings expose you to emerging **analysis techniques**, **data visualization** best practices, and evolving industry standards. -* **Join Online Communities:** Become active in Slack channels, Discord servers, or professional associations where **data analysts** share resources, job leads, and **actionable insights**. Regularly review community channels for project feedback, collaboration opportunities, and announcements about **entry-level data analyst** roles. -* **Find a Mentor or Buddy:** Seek out mentorship programs through bootcamps or university-alumni networks. A mentor, someone like a **senior data analyst** or BI (**business intelligence) analyst**, can provide personalized advice on your **data analysis life cycle**, resume reviews, and mock interviews. -* **Share Your Knowledge:** Contribute to blogs, record tutorials, or present lightning talks at virtual events. While you might just be starting, chances are someone else is going through the same process, and learning how you solved it can help them. Also, creating content like this will force you to truly understand the topic you're covering before being able to explain it. So even while you're teaching others, you're also cementing that understanding within you. - -Networking is not easy, and when you're just getting started, it might seem even harder than getting an actual data analyst job. The above list is just a set of options, you don't have to go through all of them (or any, to be honest), just find a way that works for you and follow that path. - -## Step 6: Continuous Learning and Career Development - -The analytics industry is constantly evolving, and your professional growth must keep up accordingly. Continuous learning is probably one of the only ways to ensure you stay ahead of emerging trends, refine your existing skills, and prepare for more advanced roles. - -### Lifelong Learning and Advanced Skills - -You should embrace a mindset of lifelong learning by deepening your technical expertise over time. Accept that you'll never be done learning and revisit fundamental tools like SQL and master advanced features such as window functions and recursive queries. - -Expand your proficiency in Python by exploring libraries like pandas for data manipulation and scikit-learn for machine learning workflows. If you prefer R, dive into packages like tidyverse for efficient data processing and ggplot2 for sophisticated visualizations. - -Additionally, enroll in specialized courses on machine learning algorithms, cloud-based analytics platforms (e.g., AWS Redshift, Google BigQuery), and emerging open-source tools. Staying informed through industry blogs, podcasts, and research publications will help you hone your skills in both data science and data mining. - -### Charting Your Career Path - -Once you have a solid foundation in data analysis techniques, the next step is to start mapping out your career trajectory. In other words: where do you want to go? - -Initially, you might specialize as a BI analyst, crafting interactive dashboards and KPI reports that inform strategic decisions. - -Next, consider roles such as sales data analyst, where you'll focus on revenue analytics and customer behavior modeling, or healthcare analyst, tasked with evaluating patient outcomes and ensuring regulatory compliance. - -As you gain experience, you might want to pivot toward senior data analyst or data scientist positions, taking on end-to-end projects that incorporate predictive modeling, anomaly detection, and advanced statistical analysis. - -### Setting Goals and Measuring Progress - -You can't know if you're moving in the right direction if you don't measure your progress. It's a very common mistake many new analysts make, and one you should try to avoid. - -Define clear, measurable objectives to guide your learning journey. Set milestones—such as completing a certification, publishing a portfolio project, or mastering a new visualization tool (and celebrate each achievement once you get it). - -Keep a learning journal or log within your portfolio to document projects, record the techniques applied, and reflect on lessons learned. - -Regularly try to get feedback from peers and mentors through code reviews, presentations, or mock interviews to identify areas for improvement and validate your progress. - -### Mentorship and Community Involvement - -Long-term success in data analysis relies on strong professional relationships and community engagement. Continue participating in forums, meetups, and virtual conferences to exchange insights with both aspiring analysts and seasoned professionals. - -Seek opportunities to mentor newcomers, contribute to open-source or community-driven data projects, and share your expertise through blog posts or lightning talks. - -Teaching concepts such as data cleansing, analysis workflows, and visualization best practices not only reinforces your own knowledge but also establishes your reputation as a qualified data professional in the broader data analyst job market. - -Try to incorporate these strategies into your routine; that way, you'll enhance your data analyst skills dynamically, showcase your commitment to growth, and position yourself for roles with greater responsibility and impact. - -## Conclusion - -Embarking on the path of how to become a data analyst with no experience may seem challenging, yes, but by leveraging structured learning pathways like the [Data Analyst Roadmap](https://roadmap.sh/data-analyst) and our [Mastering SQL course](https://roadmap.sh/courses/sql), you'll build the core technical foundation. - -Complementing these tools with thoughtfully chosen data analysis projects in your portfolio will validate your practical skills and demonstrate your ability to extract actionable insights from complex datasets (which is the end goal of a good data analyst). - -Beyond technical proficiency, remember that confidence and communication are equally critical. Whether you're preparing for your first entry-level data analyst interview or collaborating as a junior data analyst on real-world projects, articulating your data analysis process will set you apart. - -Practical experiences, such as internships or community-driven challenges, will further reinforce your résumé and show your commitment to continuous growth in the analytics industry. - -Every day, countless developers and analysts successfully transition into data roles by following these strategies. - -Now it's your turn: take the first step, mapping out your learning journey, and launching your own data analytics projects. Your journey from aspiring analyst to qualified data professional starts today! - diff --git a/src/data/guides/data-analyst-vs-data-engineer.md b/src/data/guides/data-analyst-vs-data-engineer.md deleted file mode 100644 index 4b8a8a6c7..000000000 --- a/src/data/guides/data-analyst-vs-data-engineer.md +++ /dev/null @@ -1,231 +0,0 @@ ---- -title: 'Data Analyst vs. Data Engineer: Skills, Salary, and more' -description: 'Compare data analyst and data engineer roles in terms of skills, tools, salaries, and career paths. Find the best fit for your goals with this complete guide.' -authorId: ekene -excludedBySlug: '/data-analyst/vs-data-engineer' -seo: - title: 'Data Analyst vs. Data Engineer: Skills, Salary, and more' - description: 'Compare data analyst and data engineer roles in terms of skills, tools, salaries, and career paths. Find the best fit for your goals with this complete guide.' - ogImageUrl: 'https://assets.roadmap.sh/guest/data-analyst-vs-data-engineer-kfov9.jpg' -isNew: false -type: 'textual' -date: 2025-08-07 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Data Analyst vs Data Engineer compared](https://assets.roadmap.sh/guest/data-analyst-vs-data-engineer-kfov9.jpg) - -If you're considering a career in data, you've likely come across two common but often confusing roles: [data analyst](https://roadmap.sh/data-analyst) and data engineer. Both play a critical role when it comes to getting insights from data, but they differ in required skills, focus, earning potential, and career path. Analysts turn data into business insights, while engineers build the infrastructure that makes that possible. - -**TL;DR** - -- **Focus:** - - **Data Analyst:** Business insights, reporting, data storytelling. - - **Data Engineer:** Data pipelines, architecture, infrastructure. -- **Skills:** - - **Analyst:** SQL, Excel, data visualization, basic Python/R. - - **Engineer:** Python/Scala, SQL, ETL tools, cloud platforms. -- **Output:** - - **Analyst:** Dashboards, reports, insights. - - **Engineer:** Data warehouses, pipelines, clean datasets. -- **Earning Potential:** - - Engineers generally earn more due to higher technical complexity. -- **Best Fit For:** - - **Analyst:** You like interpreting data and influencing decisions. - - **Engineer:** You enjoy building systems and working with code. - - -## Data analyst vs. data engineer - -The table below highlights the major differences between data analysis and data engineering to help you evaluate which one might be the right fit for you: - -| **Category** | **Data Analyst** | **Data Engineer** | -| --------------------- | ----------------------------------------------------------------- | --------------------------------------------------------------------------------- | -| **Focus** | Interpreting data to answer business questions | Building and maintaining data infrastructure | -| **Typical output** | Dashboards, reports, visualizations | ETL pipelines, data warehouses, streaming systems | -| **Key tools** | Excel, SQL, Python (pandas), Tableau, Power BI | Python, SQL, Airflow, Spark, Snowflake, dbt, Kafka | -| **Collaboration** | Works closely with business stakeholders, product managers | Works with data teams, software engineers, DevOps | -| **Career path** | Junior Analyst → Senior Analyst → Analytics Lead → Strategy Roles | Junior Engineer → Senior Engineer → Data Architect → ML Engineer or Platform Lead | -| **Coding required?** | Moderate (SQL, Python for analysis) | High (Python, Scala, Java, infrastructure scripting) | -| **Work style** | Cross-functional, presentation-heavy | Technical, system-focused | -| **Salary range (US)** | $75K–110K | $95K–135K | - -Before looking at these aspects in detail, let's take a closer look at these two fields. - -## What is data analysis? - -Data analysis is the process of examining, cleaning, transforming, and modeling data to uncover useful insights, draw conclusions, and support smarter decision-making. It's all about making sense of raw information using a mix of statistical analysis and logical techniques. - -In practice, data analysis generally falls into four main categories: - -- **Descriptive analysis:** This type of analysis looks at historical data to figure out "*what happened**.**"* Think of it as a snapshot of past events. For example, if you're running an e-commerce store, descriptive analysis might help you find the average number of sales per product last month. It's about getting a clear summary of the past. -- **Diagnostic analysis:** In this type of analysis, you're digging deeper to understand "*why something happened**.**"* Let's say a product that was selling well suddenly dropped in sales last quarter; a diagnostic analysis would involve examining other business data, like customer reviews, marketing spend, or competitor pricing, to find possible causes. -- **Predictive analysis:** This is all about forecasting based on past data and asking questions like *"**what's likely to happen next?"* For example, if you notice that people tend to buy workout gear more often in January, predictive analysis can help you prepare your inventory for that trend. -- **Prescriptive analysis:** This goes a step further by asking: "*What should we do about it**?"* For instance, if a product consistently sells out within days, prescriptive analysis might recommend increasing inventory or adjusting the supply chain to meet demand more efficiently. - -![Data Analysis Categories](https://assets.roadmap.sh/guest/data-analysis-categories-22zmq.png) - - -Regardless of the type of data analysis you conduct, most data analysis processes follow a similar approach. Here's a typical flow: - -1. **Collecting data:** This step involves gathering information from various sources, such as surveys, sensors, database systems, and web analytics, and then consolidating it into a single location, such as a spreadsheet, data warehouse, or cloud platform. The key is to make the data accessible and ready for the next steps. -2. **Cleaning data:** Raw data is rarely perfect. It often comes with missing values, duplicates, errors, or inconsistencies. Cleaning helps you fix these issues so that the analysis doesn't produce misleading results. For example, imagine one record says "New York" while another says "new yorK"; they should clearly mean the same thing, but a computer won't always treat them that way. -3. **Exploratory data analysis (EDA):** At this stage, you're trying to understand the structure of the data. What are the trends? Are there any outliers? What relationships exist between variables? You might create charts, calculate averages, or explore distributions to start forming hypotheses. For instance, you could visualize customer age vs. average spending to identify patterns. -4. **Modeling data:** This is where you apply statistical methods or machine learning algorithms. The goal could be to test a theory, measure uncertainty, or predict future outcomes. For example, a retailer might use regression analysis to understand how pricing affects demand. -5. **Interpretation and communication:** The final step is making the findings clear and actionable, especially for stakeholders who may not have a technical background. This often involves creating data visualization dashboards, writing reports, or giving presentations that turn raw numbers into meaningful stories. At the end of the day, the purpose of data analysis is to help people make informed decisions. - -## What is data engineering? - -Data engineering is all about building the backbone that makes data usable. It involves designing, developing, and maintaining the systems that enable organizations to collect, store, and process data. These systems support everything from analytics and reporting to machine learning and other data-driven applications. - -While a data analyst's primary role focuses on ***what the data means***, data engineers ensure that the data is received in the right format, at the right time, and in the right place. Think of it like setting up the pipes and water tanks before anyone can start filling cups. - -![Data engineering processing data for use](https://assets.roadmap.sh/guest/data-engineering-processing-data-for-use-oovuv.png) - - -The following is a breakdown of what data engineering typically involves: - - -1. **Data collection and ingestion:** This step involves integrating data from various sources, including databases, APIs, IoT sensors, web scraping tools, and real-time event streams, among others. A good ingestion pipeline handles various data formats and keeps data flowing reliably. -2. **Data storage and architecture:** Once data is collected, it needs a place to live. This involves setting up storage systems like data lakes or data warehouses. The goal here is to store large volumes of data in a way that's organized, efficient, and accessible for future use. -3. **Data processing and transformation:** Raw data isn't always pretty. Data engineers build ETL (Extract, Transform, Load) or ELT pipelines to clean, standardize, and reformat the data. This step involves removing duplicates, filling in missing values, correcting formats, and even aggregating data into more useful forms. -4. **Infrastructure and scalability:** As data grows, systems need to scale. This means setting up infrastructure that can handle increased traffic, more complex processing, and a growing number of users. Data engineers often work with cloud platforms (like AWS), distributed systems (like Spark), and orchestration tools (like Airflow). -5. **Data quality and monitoring:** Even the best systems can fail, and dirty data can sneak in. Part of the data engineer's job is to monitor data pipelines, detect when something breaks, and set up alerts or validation checks. This helps catch problems early before they escalate into bad decisions or broken applications. - - -## Data analyst vs. data engineer: The overlap and boundary - -The boundary between data engineers and data analysts generally lies in the data lifecycle. Data engineers focus on building the infrastructure needed to collect, store, and process data. Their responsibilities include setting up data pipelines, managing database management systems, and transforming raw data into structured, usable formats. Data analysts, on the other hand, step in once the data is accessible and organized. They work on interpreting the data, uncovering patterns, and generating insights that can drive business decisions. - - -![Shared skills between data analyst and data engineer](https://assets.roadmap.sh/guest/shared-skills-between-data-analyst-and-data-engineer-ghk6p.png) - - -However, this distinction isn't always clear-cut. In many modern teams, analysts often take on light data engineering tasks. For example, they might write simple ETL scripts or use tools like SQL or dbt to prepare datasets for analysis. At the same time, data engineers sometimes run basic queries or build simple dashboards to validate that their pipelines are functioning properly and delivering accurate data. - -In practice, while the core responsibilities of engineers and analysts are different, there is often a lot of overlap. Collaboration between the two roles is common and necessary, especially as tools and workflows continue to evolve. The table below shows the similarities between a data analyst and a data engineer: - -| **Area** | **Shared Aspects** | -| --------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **Goal-oriented** | Both roles support the broader goal of helping the organization make informed, data-driven decisions. | -| **Working with Data** | Each role involves interacting with large datasets, including structured and unstructured data stored in databases or data warehouses. | -| **Use of SQL** | SQL is a key skill for both. Analysts use it to extract and analyze data, while engineers use it to design, build, and manage data pipelines. | -| **Data cleaning** | Both deal with messy or inconsistent data, such as missing values or incorrect formats, and work to clean and standardize it. | -| **Collaboration** | Engineers build the data infrastructure that analysts depend on. They often collaborate closely, especially when working on the same products or business goals. | -| **Problem solving** | Both roles require strong analytical thinking and a solution-oriented approach to address data-related challenges. | -| **Documentation** | Each role involves documenting processes, logic, and workflows to ensure transparency, reproducibility, and easy handoffs between teams. | - -Now that we've covered data analysis and data engineering regarding what they are, the processes they follow, and similarities, let's explore why you might want to choose one over the other. - -## What does your day-to-day involve, and how do you collaborate with others? - -If you enjoy building things from the ground up or find satisfaction in setting up systems that run quietly in the background, helping automate repetitive tasks and improve performance, then data engineering might be a great fit for you. You'll likely feel at home in this role because, as a data engineer, you'll spend most of your time designing and maintaining the infrastructure that allows data to flow reliably. You'll build pipelines, work with data analysis tools, and handle tasks like batch processing, real-time streaming, and data warehousing. - -On the other hand, if you're someone who lights up at the sight of a messy spreadsheet and immediately wants to dig in to find trends, ask questions, and organize it for others, then data analysis might be more your thing. Data analysts focus on interpreting data and sharing insights with stakeholders so they can make informed decisions. - - -![Data analyst vs data engineer day-to-day](https://assets.roadmap.sh/guest/data-analysts-vs-data-engineer-day-to-day-w9u3k.png) - - -In addition, both data analysts and data engineers work in collaborative environments, but how and with whom they interact can vary. The way you prefer to work with others might reveal which path suits you best. - -If you enjoy working closely with business teams and answering questions like "Why did sales drop last quarter?" or "Which customer segment is most profitable?", then the data analyst role might feel more natural to you. Analysts often sit at the intersection of data and decision-making. You'll collaborate with marketing, product, operations, and leadership teams to interpret trends, explain reports, and communicate insights clearly. - -However, suppose you prefer working with technical teammates such as developers, DevOps engineers, and other data professionals to build robust systems and solve backend challenges. In that case, the data engineer role may be a better fit. As an engineer, you'll focus more on long-term technical projects and communicate primarily with other technical stakeholders. Your work will revolve around performance, scalability, and the structure that supports the entire data ecosystem. - - -## What's your background? - -If your background is in statistics, economics, or mathematics, that gives you a strong foundation for a data analyst role. These fields develop analytical thinking and a solid grasp of working with numbers. Degrees in psychology, sociology, or other sciences are also a great fit, especially when your work involves understanding customer behavior or conducting market research. - -If you come from a background in computer science, physics, software engineering, systems engineering, or any IT-related field that blends coding with system design, then data engineering could be a better match. This role leans more toward building and maintaining the technical systems that power data operations at scale. - -## What skills and technologies will you be using? - -The tools you enjoy using and the skills you naturally lean toward can offer clues about which role aligns with you. - -If you're drawn to visual tools, storytelling with data, and business intelligence platforms, the data analyst path might be the way to go. You'll likely work with tools like Excel, [SQL](https://roadmap.sh/sql), Power BI, Tableau, or Looker. Analysts write queries to pull and clean data, then use dashboards, charts, and reports to present insights. A solid grasp of statistics and critical thinking will help you understand the "why" behind the data, and you'll need to clearly communicate your findings to help teams make smart decisions. - -If you're more excited by code, infrastructure, and scaling systems, then data engineering might be more your style. You'll be working with programming languages like [Python](https://roadmap.sh/python) and SQL, and tools like Spark, Airflow, dbt, and platforms such as [AWS](https://roadmap.sh/aws), Azure, or Google Cloud. Engineers build and maintain data pipelines, data warehouses, and Extract, Load, and Transform (ELT) processes. It's a deeply technical role, so having strong software development habits, version control skills (like [Git](https://roadmap.sh/git-github)), and a focus on automation will go a long way. - -Although the technical tools differ between these two paths, there's one shared skill that often gets overlooked: **communication**. Whether you're analyzing data or engineering the systems that deliver it, you'll need to explain your work in a way that makes sense to others. Your stakeholders usually aren't interested in the fancy tools or algorithms you used. They want clear, actionable insights that help them understand the story behind the numbers. - -**How to get started?** -Looking at the tools and skills listed for both roles can feel overwhelming at first. Trust me, you're not alone. I had the same dilemma when I was starting my career. The key is to start small and not get lost in the sea of technologies. - -A great starting point is SQL. It's a core skill needed by both data analysts and engineers, which is why we created a beginner-friendly [SQL course](https://roadmap.sh/courses/sql) designed to take you from zero to hero. It's hands-on and runs entirely in your browser, so you won't need to install any software to get started. - -After the SQL course, another good starting point is the [Google](https://grow.google/certificates/en_uk/certificates/data-analytics) data analytics certification for data analysts and [data engineering bootcamp](https://dataengineercamp.com/) for data engineers. - -## Data engineer vs. data analyst salary and career progression - -Both data analyst and data engineer roles offer rewarding career paths with plenty of opportunities for growth and specialization. In general, data engineers tend to earn more than data analysts. This is largely due to the complexity of their responsibilities, which often involve infrastructure, system performance, and solving large-scale data problems that require deeper technical skills. - -As a data analyst, you can work in roles that carry strategic influence or domain-specific expertise in areas like product, marketing, or finance. With the rise of data literacy in many organizations, analysts now have more visibility across departments and often collaborate directly with decision-makers. This cross-functional exposure can lead to quicker growth and expanded responsibilities over time. - -A typical career progression for a data analyst might look like: - -- Junior Data Analyst -- Data Analyst -- Senior Data Analyst -- Analytics Manager / Data Product Manager -- Head of Analytics / Director of Data - -The average salary of a data analyst in the United States is approximately [$83,916 per year](https://www.indeed.com/career/data-analyst/salaries), with room for growth as you gain more experience and develop advanced analytical skills. - -![Data analyst salary per annum](https://assets.roadmap.sh/guest/data-analyst-salary-per-annum-xkq34.png) - - -Data engineering roles open doors into the world of infrastructure, architecture, and systems that power the entire data lifecycle within a company. Organizations that rely heavily on data often require engineers to build and maintain data architecture that keeps everything running smoothly. - -A typical data engineering career path includes: - -- Junior Data Engineer -- Data Engineer -- Senior Data Engineer -- Lead / Staff Data Engineer -- Data Engineering Manager - -The average salary of a data engineer in the United States is about [$128,745 per annum](https://www.indeed.com/career/data-engineer/salaries?from=top_sb), reflecting the technical demands and backend complexity of the role. - -![Data engineer salary per annum](https://assets.roadmap.sh/guest/data-engineer-salary-per-annum-ud5oa.png) - - -**Potential transition: Can a data analyst become a data engineer?** -Yes, transitioning from data analysis to data engineering is absolutely possible. Over time, you may find yourself curious about other areas that are adjacent to your current role. As a data analyst, you're already working closely with data management, and you likely have solid experience in SQL, data manipulation, cleaning, and modeling. These foundational skills make it easier to pivot toward data engineering. - -You also have a general sense of how data flows through systems, which gives you a head start when learning about building infrastructure, setting up pipelines, and maintaining performance at scale. - -Other career paths you can explore as a data analyst include: - -- Data Scientist -- Product Analyst -- Business Intelligence Lead - -For data engineers, your hands-on experience with data architecture, automation, and data platforms opens the door to roles like: - -- Machine Learning Engineer -- Platform Engineer -- Technical Architect - - -## Making a decision - -Ultimately, both data analysis and data engineering are rewarding and in-demand career paths. To figure out which one truly fits you, try giving yourself a litmus test: - -- Try querying a dataset using SQL, creating visualizations with tools like Tableau or Power BI, and presenting insights in a clear narrative. If you find yourself energized by telling stories with data and helping others make smarter decisions, data analysis may be the path for you. -- Try building a simple data pipeline using Python, where you clean and transform raw data, and store it in a database. If you're more excited by the challenge of making messy data usable and scalable, data engineering could be your ideal direction. - -Whichever field you go with, remember that you don't have to choose one permanently. Many professionals start in one role and transition to another as their interests evolve. - -Start here: - -- [SQL basic to advanced course](https://roadmap.sh/courses/sql) -- [SQL roadmap](https://roadmap.sh/sql) -- [Data analyst roadmap](https://roadmap.sh/data-analyst) \ No newline at end of file diff --git a/src/data/guides/design-patterns-for-humans.md b/src/data/guides/design-patterns-for-humans.md deleted file mode 100644 index d3eace516..000000000 --- a/src/data/guides/design-patterns-for-humans.md +++ /dev/null @@ -1,2428 +0,0 @@ ---- -title: 'Design Patterns for Humans' -description: 'A language agnostic, ultra-simplified explanation to design patterns' -authorId: 'kamran' -seo: - title: 'Design Patterns for Humans - roadmap.sh' - description: 'A language agnostic, ultra-simplified explanation to design patterns' -isNew: false -type: 'textual' -date: 2019-01-23 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -Design patterns are solutions to recurring problems; **guidelines on how to tackle certain problems**. They are not classes, packages or libraries that you can plug into your application and wait for the magic to happen. These are, rather, guidelines on how to tackle certain problems in certain situations. - -> Design patterns are solutions to recurring problems; guidelines on how to tackle certain problems - -Wikipedia describes them as - -> In software engineering, a software design pattern is a general reusable solution to a commonly occurring problem within a given context in software design. It is not a finished design that can be transformed directly into source or machine code. It is a description or template for how to solve a problem that can be used in many different situations. - -## Be Careful - -Developers, mostly beginners, make the mistake of over-thinking and forcing the design patterns which results in a horrible un-maintainable mess. The rule of thumb is to keep the codebase as simple as possible, once you start developing, you will start to see the patterns repeating in the codebase in which case you can go ahead and implement the relevant design patterns. - -- Design patterns are not a silver bullet to all your problems. -- Do not try to force them; bad things are supposed to happen, if done so. -- Keep in mind that design patterns are solutions **to** problems, not solutions **finding** problems; so don't overthink. -- If used in a correct place in a correct manner, they can prove to be a savior; or else they can result in a horrible mess of a code. - -> Also note that the code samples below are in PHP-7, however this shouldn't stop you because the concepts are same anyways. - -## Types of Design Patterns - -This guide is about Gang of Four (GoF) design patterns, which refers to the four authors of [the book which introduced these design patterns](https://en.wikipedia.org/wiki/Design_Patterns). There are three types of design patterns: - -- [Creational](#creational-design-patterns) -- [Structural](#structural-design-patterns) -- [Behavioral](#behavioral-design-patterns) - -## Creational Design Patterns - -In plain words - -> Creational patterns are focused towards how to instantiate an object or group of related objects. - -Wikipedia says - -> In software engineering, creational design patterns are design patterns that deal with object creation mechanisms, trying to create objects in a manner suitable to the situation. The basic form of object creation could result in design problems or added complexity to the design. Creational design patterns solve this problem by somehow controlling this object creation. - -There are 6 types of Creational patterns - -- [Simple Factory](#-simple-factory) -- [Factory Method](#-factory-method) -- [Abstract Factory](#-abstract-factory) -- [Builder](#-builder) -- [Prototype](#-prototype) -- [Singleton](#-singleton) - -## 🏠 Simple Factory - -Real world example - -> Consider, you are building a house and you need doors. You can either put on your carpenter clothes, bring some wood, glue, nails and all the tools required to build the door and start building it in your house or you can simply call the factory and get the built door delivered to you so that you don't need to learn anything about the door making or to deal with the mess that comes with making it. - -In plain words - -> Simple factory simply generates an instance for client without exposing any instantiation logic to the client - -Wikipedia says - -> In object-oriented programming (OOP), a factory is an object for creating other objects – formally a factory is a function or method that returns objects of a varying prototype or class from some method call, which is assumed to be "new". - -**Programmatic Example** - -First of all we have a door interface and the implementation - -```php -interface Door -{ - public function getWidth(): float; - public function getHeight(): float; -} - -class WoodenDoor implements Door -{ - protected $width; - protected $height; - - public function __construct(float $width, float $height) - { - $this->width = $width; - $this->height = $height; - } - - public function getWidth(): float - { - return $this->width; - } - - public function getHeight(): float - { - return $this->height; - } -} -``` - -Then we have our door factory that makes the door and returns it - -```php -class DoorFactory -{ - public static function makeDoor($width, $height): Door - { - return new WoodenDoor($width, $height); - } -} -``` - -And then it can be used as - -```php -// Make me a door of 100x200 -$door = DoorFactory::makeDoor(100, 200); - -echo 'Width: ' . $door->getWidth(); -echo 'Height: ' . $door->getHeight(); - -// Make me a door of 50x100 -$door2 = DoorFactory::makeDoor(50, 100); -``` - -**When to Use?** - -When creating an object is not just a few assignments and involves some logic, it makes sense to put it in a dedicated factory instead of repeating the same code everywhere. - -## 🏭 Factory Method - -Real world example - -> Consider the case of a hiring manager. It is impossible for one person to interview for each of the positions. Based on the job opening, she has to decide and delegate the interview steps to different people. - -In plain words - -> It provides a way to delegate the instantiation logic to child classes. - -Wikipedia says - -> In class-based programming, the factory method pattern is a creational pattern that uses factory methods to deal with the problem of creating objects without having to specify the exact class of the object that will be created. This is done by creating objects by calling a factory method—either specified in an interface and implemented by child classes, or implemented in a base class and optionally overridden by derived classes—rather than by calling a constructor. - -**Programmatic Example** - -Taking our hiring manager example above. First of all we have an interviewer interface and some implementations for it - -```php -interface Interviewer -{ - public function askQuestions(); -} - -class Developer implements Interviewer -{ - public function askQuestions() - { - echo 'Asking about design patterns!'; - } -} - -class CommunityExecutive implements Interviewer -{ - public function askQuestions() - { - echo 'Asking about community building'; - } -} -``` - -Now let us create our `HiringManager` - -```php -abstract class HiringManager -{ - - // Factory method - abstract protected function makeInterviewer(): Interviewer; - - public function takeInterview() - { - $interviewer = $this->makeInterviewer(); - $interviewer->askQuestions(); - } -} - -``` - -Now any child can extend it and provide the required interviewer - -```php -class DevelopmentManager extends HiringManager -{ - protected function makeInterviewer(): Interviewer - { - return new Developer(); - } -} - -class MarketingManager extends HiringManager -{ - protected function makeInterviewer(): Interviewer - { - return new CommunityExecutive(); - } -} -``` - -and then it can be used as - -```php -$devManager = new DevelopmentManager(); -$devManager->takeInterview(); // Output: Asking about design patterns - -$marketingManager = new MarketingManager(); -$marketingManager->takeInterview(); // Output: Asking about community building. -``` - -**When to use?** - -Useful when there is some generic processing in a class but the required sub-class is dynamically decided at runtime. Or putting it in other words, when the client doesn't know what exact sub-class it might need. - -## 🔨 Abstract Factory - -Real world example - -> Extending our door example from Simple Factory. Based on your needs you might get a wooden door from a wooden door shop, iron door from an iron shop or a PVC door from the relevant shop. Plus you might need a guy with different kind of specialities to fit the door, for example a carpenter for wooden door, welder for iron door etc. As you can see there is a dependency between the doors now, wooden door needs carpenter, iron door needs a welder etc. - -In plain words - -> A factory of factories; a factory that groups the individual but related/dependent factories together without specifying their concrete classes. - -Wikipedia says - -> The abstract factory pattern provides a way to encapsulate a group of individual factories that have a common theme without specifying their concrete classes - -**Programmatic Example** - -Translating the door example above. First of all we have our `Door` interface and some implementation for it - -```php -interface Door -{ - public function getDescription(); -} - -class WoodenDoor implements Door -{ - public function getDescription() - { - echo 'I am a wooden door'; - } -} - -class IronDoor implements Door -{ - public function getDescription() - { - echo 'I am an iron door'; - } -} -``` - -Then we have some fitting experts for each door type - -```php -interface DoorFittingExpert -{ - public function getDescription(); -} - -class Welder implements DoorFittingExpert -{ - public function getDescription() - { - echo 'I can only fit iron doors'; - } -} - -class Carpenter implements DoorFittingExpert -{ - public function getDescription() - { - echo 'I can only fit wooden doors'; - } -} -``` - -Now we have our abstract factory that would let us make family of related objects i.e. wooden door factory would create a wooden door and wooden door fitting expert and iron door factory would create an iron door and iron door fitting expert - -```php -interface DoorFactory -{ - public function makeDoor(): Door; - public function makeFittingExpert(): DoorFittingExpert; -} - -// Wooden factory to return carpenter and wooden door -class WoodenDoorFactory implements DoorFactory -{ - public function makeDoor(): Door - { - return new WoodenDoor(); - } - - public function makeFittingExpert(): DoorFittingExpert - { - return new Carpenter(); - } -} - -// Iron door factory to get iron door and the relevant fitting expert -class IronDoorFactory implements DoorFactory -{ - public function makeDoor(): Door - { - return new IronDoor(); - } - - public function makeFittingExpert(): DoorFittingExpert - { - return new Welder(); - } -} -``` - -And then it can be used as - -```php -$woodenFactory = new WoodenDoorFactory(); - -$door = $woodenFactory->makeDoor(); -$expert = $woodenFactory->makeFittingExpert(); - -$door->getDescription(); // Output: I am a wooden door -$expert->getDescription(); // Output: I can only fit wooden doors - -// Same for Iron Factory -$ironFactory = new IronDoorFactory(); - -$door = $ironFactory->makeDoor(); -$expert = $ironFactory->makeFittingExpert(); - -$door->getDescription(); // Output: I am an iron door -$expert->getDescription(); // Output: I can only fit iron doors -``` - -As you can see the wooden door factory has encapsulated the `carpenter` and the `wooden door` also iron door factory has encapsulated the `iron door` and `welder`. And thus it had helped us make sure that for each of the created door, we do not get a wrong fitting expert. - -**When to use?** - -When there are interrelated dependencies with not-that-simple creation logic involved - -## 👷 Builder - -Real world example - -> Imagine you are at Hardee's and you order a specific deal, lets say, "Big Hardee" and they hand it over to you without _any questions_; this is the example of simple factory. But there are cases when the creation logic might involve more steps. For example you want a customized Subway deal, you have several options in how your burger is made e.g what bread do you want? what types of sauces would you like? What cheese would you want? etc. In such cases builder pattern comes to the rescue. - -In plain words - -> Allows you to create different flavors of an object while avoiding constructor pollution. Useful when there could be several flavors of an object. Or when there are a lot of steps involved in creation of an object. - -Wikipedia says - -> The builder pattern is an object creation software design pattern with the intentions of finding a solution to the telescoping constructor anti-pattern. - -Having said that let me add a bit about what telescoping constructor anti-pattern is. At one point or the other we have all seen a constructor like below: - -```php -public function __construct($size, $cheese = true, $pepperoni = true, $tomato = false, $lettuce = true) -{ -} -``` - -As you can see; the number of constructor parameters can quickly get out of hand and it might become difficult to understand the arrangement of parameters. Plus this parameter list could keep on growing if you would want to add more options in future. This is called telescoping constructor anti-pattern. - -**Programmatic Example** - -The sane alternative is to use the builder pattern. First of all we have our burger that we want to make - -```php -class Burger -{ - protected $size; - - protected $cheese = false; - protected $pepperoni = false; - protected $lettuce = false; - protected $tomato = false; - - public function __construct(BurgerBuilder $builder) - { - $this->size = $builder->size; - $this->cheese = $builder->cheese; - $this->pepperoni = $builder->pepperoni; - $this->lettuce = $builder->lettuce; - $this->tomato = $builder->tomato; - } -} -``` - -And then we have the builder - -```php -class BurgerBuilder -{ - public $size; - - public $cheese = false; - public $pepperoni = false; - public $lettuce = false; - public $tomato = false; - - public function __construct(int $size) - { - $this->size = $size; - } - - public function addPepperoni() - { - $this->pepperoni = true; - return $this; - } - - public function addLettuce() - { - $this->lettuce = true; - return $this; - } - - public function addCheese() - { - $this->cheese = true; - return $this; - } - - public function addTomato() - { - $this->tomato = true; - return $this; - } - - public function build(): Burger - { - return new Burger($this); - } -} -``` - -And then it can be used as: - -```php -$burger = (new BurgerBuilder(14)) - ->addPepperoni() - ->addLettuce() - ->addTomato() - ->build(); -``` - -**When to use?** - -When there could be several flavors of an object and to avoid the constructor telescoping. The key difference from the factory pattern is that; factory pattern is to be used when the creation is a one step process while builder pattern is to be used when the creation is a multi step process. - -## 🐑 Prototype - -Real world example - -> Remember dolly? The sheep that was cloned! Lets not get into the details but the key point here is that it is all about cloning - -In plain words - -> Create object based on an existing object through cloning. - -Wikipedia says - -> The prototype pattern is a creational design pattern in software development. It is used when the type of objects to create is determined by a prototypical instance, which is cloned to produce new objects. - -In short, it allows you to create a copy of an existing object and modify it to your needs, instead of going through the trouble of creating an object from scratch and setting it up. - -**Programmatic Example** - -In PHP, it can be easily done using `clone` - -```php -class Sheep -{ - protected $name; - protected $category; - - public function __construct(string $name, string $category = 'Mountain Sheep') - { - $this->name = $name; - $this->category = $category; - } - - public function setName(string $name) - { - $this->name = $name; - } - - public function getName() - { - return $this->name; - } - - public function setCategory(string $category) - { - $this->category = $category; - } - - public function getCategory() - { - return $this->category; - } -} -``` - -Then it can be cloned like below - -```php -$original = new Sheep('Jolly'); -echo $original->getName(); // Jolly -echo $original->getCategory(); // Mountain Sheep - -// Clone and modify what is required -$cloned = clone $original; -$cloned->setName('Dolly'); -echo $cloned->getName(); // Dolly -echo $cloned->getCategory(); // Mountain sheep -``` - -Also you could use the magic method `__clone` to modify the cloning behavior. - -**When to use?** - -When an object is required that is similar to existing object or when the creation would be expensive as compared to cloning. - -## 💍 Singleton - -Real world example - -> There can only be one president of a country at a time. The same president has to be brought to action, whenever duty calls. President here is singleton. - -In plain words - -> Ensures that only one object of a particular class is ever created. - -Wikipedia says - -> In software engineering, the singleton pattern is a software design pattern that restricts the instantiation of a class to one object. This is useful when exactly one object is needed to coordinate actions across the system. - -Singleton pattern is actually considered an anti-pattern and overuse of it should be avoided. It is not necessarily bad and could have some valid use-cases but should be used with caution because it introduces a global state in your application and change to it in one place could affect in the other areas and it could become pretty difficult to debug. The other bad thing about them is it makes your code tightly coupled plus mocking the singleton could be difficult. - -**Programmatic Example** - -To create a singleton, make the constructor private, disable cloning, disable extension and create a static variable to house the instance - -```php -final class President -{ - private static $instance; - - private function __construct() - { - // Hide the constructor - } - - public static function getInstance(): President - { - if (!self::$instance) { - self::$instance = new self(); - } - - return self::$instance; - } - - private function __clone() - { - // Disable cloning - } - - private function __wakeup() - { - // Disable unserialize - } -} -``` - -Then in order to use - -```php -$president1 = President::getInstance(); -$president2 = President::getInstance(); - -var_dump($president1 === $president2); // true -``` - -## Structural Design Patterns - -In plain words - -> Structural patterns are mostly concerned with object composition or in other words how the entities can use each other. Or yet another explanation would be, they help in answering "How to build a software component?" - -Wikipedia says - -> In software engineering, structural design patterns are design patterns that ease the design by identifying a simple way to realize relationships between entities. - -There are 7 types of structural patterns - -- [Adapter](#-adapter) -- [Bridge](#-bridge) -- [Composite](#-composite) -- [Decorator](#-decorator) -- [Facade](#-facade) -- [Flyweight](#-flyweight) -- [Proxy](#-proxy) - -## 🔌 Adapter - -Real world example - -> Consider that you have some pictures in your memory card and you need to transfer them to your computer. In order to transfer them you need some kind of adapter that is compatible with your computer ports so that you can attach memory card to your computer. In this case card reader is an adapter. -> Another example would be the famous power adapter; a three legged plug can't be connected to a two pronged outlet, it needs to use a power adapter that makes it compatible with the two pronged outlet. -> Yet another example would be a translator translating words spoken by one person to another - -In plain words - -> Adapter pattern lets you wrap an otherwise incompatible object in an adapter to make it compatible with another class. - -Wikipedia says - -> In software engineering, the adapter pattern is a software design pattern that allows the interface of an existing class to be used as another interface. It is often used to make existing classes work with others without modifying their source code. - -**Programmatic Example** - -Consider a game where there is a hunter and he hunts lions. - -First we have an interface `Lion` that all types of lions have to implement - -```php -interface Lion -{ - public function roar(); -} - -class AfricanLion implements Lion -{ - public function roar() - { - } -} - -class AsianLion implements Lion -{ - public function roar() - { - } -} -``` - -And hunter expects any implementation of `Lion` interface to hunt. - -```php -class Hunter -{ - public function hunt(Lion $lion) - { - $lion->roar(); - } -} -``` - -Now let's say we have to add a `WildDog` in our game so that hunter can hunt that also. But we can't do that directly because dog has a different interface. To make it compatible for our hunter, we will have to create an adapter that is compatible - -```php -// This needs to be added to the game -class WildDog -{ - public function bark() - { - } -} - -// Adapter around wild dog to make it compatible with our game -class WildDogAdapter implements Lion -{ - protected $dog; - - public function __construct(WildDog $dog) - { - $this->dog = $dog; - } - - public function roar() - { - $this->dog->bark(); - } -} -``` - -And now the `WildDog` can be used in our game using `WildDogAdapter`. - -```php -$wildDog = new WildDog(); -$wildDogAdapter = new WildDogAdapter($wildDog); - -$hunter = new Hunter(); -$hunter->hunt($wildDogAdapter); -``` - -## 🚡 Bridge - -Real world example - -> Consider you have a website with different pages and you are supposed to allow the user to change the theme. What would you do? Create multiple copies of each of the pages for each of the themes or would you just create separate theme and load them based on the user's preferences? Bridge pattern allows you to do the second i.e. - -![With and without the bridge pattern](https://cloud.githubusercontent.com/assets/11269635/23065293/33b7aea0-f515-11e6-983f-98823c9845ee.png) - -In Plain Words - -> Bridge pattern is about preferring composition over inheritance. Implementation details are pushed from a hierarchy to another object with a separate hierarchy. - -Wikipedia says - -> The bridge pattern is a design pattern used in software engineering that is meant to "decouple an abstraction from its implementation so that the two can vary independently" - -**Programmatic Example** - -Translating our WebPage example from above. Here we have the `WebPage` hierarchy - -```php -interface WebPage -{ - public function __construct(Theme $theme); - public function getContent(); -} - -class About implements WebPage -{ - protected $theme; - - public function __construct(Theme $theme) - { - $this->theme = $theme; - } - - public function getContent() - { - return "About page in " . $this->theme->getColor(); - } -} - -class Careers implements WebPage -{ - protected $theme; - - public function __construct(Theme $theme) - { - $this->theme = $theme; - } - - public function getContent() - { - return "Careers page in " . $this->theme->getColor(); - } -} -``` - -And the separate theme hierarchy - -```php - -interface Theme -{ - public function getColor(); -} - -class DarkTheme implements Theme -{ - public function getColor() - { - return 'Dark Black'; - } -} -class LightTheme implements Theme -{ - public function getColor() - { - return 'Off white'; - } -} -class AquaTheme implements Theme -{ - public function getColor() - { - return 'Light blue'; - } -} -``` - -And both the hierarchies - -```php -$darkTheme = new DarkTheme(); - -$about = new About($darkTheme); -$careers = new Careers($darkTheme); - -echo $about->getContent(); // "About page in Dark Black"; -echo $careers->getContent(); // "Careers page in Dark Black"; -``` - -## 🌿 Composite - -Real world example - -> Every organization is composed of employees. Each of the employees has the same features i.e. has a salary, has some responsibilities, may or may not report to someone, may or may not have some subordinates etc. - -In plain words - -> Composite pattern lets clients treat the individual objects in a uniform manner. - -Wikipedia says - -> In software engineering, the composite pattern is a partitioning design pattern. The composite pattern describes that a group of objects is to be treated in the same way as a single instance of an object. The intent of a composite is to "compose" objects into tree structures to represent part-whole hierarchies. Implementing the composite pattern lets clients treat individual objects and compositions uniformly. - -**Programmatic Example** - -Taking our employees example from above. Here we have different employee types - -```php -interface Employee -{ - public function __construct(string $name, float $salary); - public function getName(): string; - public function setSalary(float $salary); - public function getSalary(): float; - public function getRoles(): array; -} - -class Developer implements Employee -{ - protected $salary; - protected $name; - protected $roles; - - public function __construct(string $name, float $salary) - { - $this->name = $name; - $this->salary = $salary; - } - - public function getName(): string - { - return $this->name; - } - - public function setSalary(float $salary) - { - $this->salary = $salary; - } - - public function getSalary(): float - { - return $this->salary; - } - - public function getRoles(): array - { - return $this->roles; - } -} - -class Designer implements Employee -{ - protected $salary; - protected $name; - protected $roles; - - public function __construct(string $name, float $salary) - { - $this->name = $name; - $this->salary = $salary; - } - - public function getName(): string - { - return $this->name; - } - - public function setSalary(float $salary) - { - $this->salary = $salary; - } - - public function getSalary(): float - { - return $this->salary; - } - - public function getRoles(): array - { - return $this->roles; - } -} -``` - -Then we have an organization which consists of several different types of employees - -```php -class Organization -{ - protected $employees; - - public function addEmployee(Employee $employee) - { - $this->employees[] = $employee; - } - - public function getNetSalaries(): float - { - $netSalary = 0; - - foreach ($this->employees as $employee) { - $netSalary += $employee->getSalary(); - } - - return $netSalary; - } -} -``` - -And then it can be used as - -```php -// Prepare the employees -$john = new Developer('John Doe', 12000); -$jane = new Designer('Jane Doe', 15000); - -// Add them to organization -$organization = new Organization(); -$organization->addEmployee($john); -$organization->addEmployee($jane); - -echo "Net salaries: " . $organization->getNetSalaries(); // Net Salaries: 27000 -``` - -## ☕ Decorator - -Real world example - -> Imagine you run a car service shop offering multiple services. Now how do you calculate the bill to be charged? You pick one service and dynamically keep adding to it the prices for the provided services till you get the final cost. Here each type of service is a decorator. - -In plain words - -> Decorator pattern lets you dynamically change the behavior of an object at run time by wrapping them in an object of a decorator class. - -Wikipedia says - -> In object-oriented programming, the decorator pattern is a design pattern that allows behavior to be added to an individual object, either statically or dynamically, without affecting the behavior of other objects from the same class. The decorator pattern is often useful for adhering to the Single Responsibility Principle, as it allows functionality to be divided between classes with unique areas of concern. - -**Programmatic Example** - -Lets take coffee for example. First of all we have a simple coffee implementing the coffee interface - -```php -interface Coffee -{ - public function getCost(); - public function getDescription(); -} - -class SimpleCoffee implements Coffee -{ - public function getCost() - { - return 10; - } - - public function getDescription() - { - return 'Simple coffee'; - } -} -``` - -We want to make the code extensible to allow options to modify it if required. Lets make some add-ons (decorators) - -```php -class MilkCoffee implements Coffee -{ - protected $coffee; - - public function __construct(Coffee $coffee) - { - $this->coffee = $coffee; - } - - public function getCost() - { - return $this->coffee->getCost() + 2; - } - - public function getDescription() - { - return $this->coffee->getDescription() . ', milk'; - } -} - -class WhipCoffee implements Coffee -{ - protected $coffee; - - public function __construct(Coffee $coffee) - { - $this->coffee = $coffee; - } - - public function getCost() - { - return $this->coffee->getCost() + 5; - } - - public function getDescription() - { - return $this->coffee->getDescription() . ', whip'; - } -} - -class VanillaCoffee implements Coffee -{ - protected $coffee; - - public function __construct(Coffee $coffee) - { - $this->coffee = $coffee; - } - - public function getCost() - { - return $this->coffee->getCost() + 3; - } - - public function getDescription() - { - return $this->coffee->getDescription() . ', vanilla'; - } -} -``` - -Lets make a coffee now - -```php -$someCoffee = new SimpleCoffee(); -echo $someCoffee->getCost(); // 10 -echo $someCoffee->getDescription(); // Simple Coffee - -$someCoffee = new MilkCoffee($someCoffee); -echo $someCoffee->getCost(); // 12 -echo $someCoffee->getDescription(); // Simple Coffee, milk - -$someCoffee = new WhipCoffee($someCoffee); -echo $someCoffee->getCost(); // 17 -echo $someCoffee->getDescription(); // Simple Coffee, milk, whip - -$someCoffee = new VanillaCoffee($someCoffee); -echo $someCoffee->getCost(); // 20 -echo $someCoffee->getDescription(); // Simple Coffee, milk, whip, vanilla -``` - -## 📦 Facade - -Real world example - -> How do you turn on the computer? "Hit the power button" you say! That is what you believe because you are using a simple interface that computer provides on the outside, internally it has to do a lot of stuff to make it happen. This simple interface to the complex subsystem is a facade. - -In plain words - -> Facade pattern provides a simplified interface to a complex subsystem. - -Wikipedia says - -> A facade is an object that provides a simplified interface to a larger body of code, such as a class library. - -**Programmatic Example** - -Taking our computer example from above. Here we have the computer class - -```php -class Computer -{ - public function getElectricShock() - { - echo "Ouch!"; - } - - public function makeSound() - { - echo "Beep beep!"; - } - - public function showLoadingScreen() - { - echo "Loading.."; - } - - public function bam() - { - echo "Ready to be used!"; - } - - public function closeEverything() - { - echo "Bup bup bup buzzzz!"; - } - - public function sooth() - { - echo "Zzzzz"; - } - - public function pullCurrent() - { - echo "Haaah!"; - } -} -``` - -Here we have the facade - -```php -class ComputerFacade -{ - protected $computer; - - public function __construct(Computer $computer) - { - $this->computer = $computer; - } - - public function turnOn() - { - $this->computer->getElectricShock(); - $this->computer->makeSound(); - $this->computer->showLoadingScreen(); - $this->computer->bam(); - } - - public function turnOff() - { - $this->computer->closeEverything(); - $this->computer->pullCurrent(); - $this->computer->sooth(); - } -} -``` - -Now to use the facade - -```php -$computer = new ComputerFacade(new Computer()); -$computer->turnOn(); // Ouch! Beep beep! Loading.. Ready to be used! -$computer->turnOff(); // Bup bup buzzz! Haah! Zzzzz -``` - -## 🍃 Flyweight - -Real world example - -> Did you ever have fresh tea from some stall? They often make more than one cup that you demanded and save the rest for any other customer so to save the resources e.g. gas etc. Flyweight pattern is all about that i.e. sharing. - -In plain words - -> It is used to minimize memory usage or computational expenses by sharing as much as possible with similar objects. - -Wikipedia says - -> In computer programming, flyweight is a software design pattern. A flyweight is an object that minimizes memory use by sharing as much data as possible with other similar objects; it is a way to use objects in large numbers when a simple repeated representation would use an unacceptable amount of memory. - -**Programmatic example** - -Translating our tea example from above. First of all we have tea types and tea maker - -```php -// Anything that will be cached is flyweight. -// Types of tea here will be flyweights. -class KarakTea -{ -} - -// Acts as a factory and saves the tea -class TeaMaker -{ - protected $availableTea = []; - - public function make($preference) - { - if (empty($this->availableTea[$preference])) { - $this->availableTea[$preference] = new KarakTea(); - } - - return $this->availableTea[$preference]; - } -} -``` - -Then we have the `TeaShop` which takes orders and serves them - -```php -class TeaShop -{ - protected $orders; - protected $teaMaker; - - public function __construct(TeaMaker $teaMaker) - { - $this->teaMaker = $teaMaker; - } - - public function takeOrder(string $teaType, int $table) - { - $this->orders[$table] = $this->teaMaker->make($teaType); - } - - public function serve() - { - foreach ($this->orders as $table => $tea) { - echo "Serving tea to table# " . $table; - } - } -} -``` - -And it can be used as below - -```php -$teaMaker = new TeaMaker(); -$shop = new TeaShop($teaMaker); - -$shop->takeOrder('less sugar', 1); -$shop->takeOrder('more milk', 2); -$shop->takeOrder('without sugar', 5); - -$shop->serve(); -// Serving tea to table# 1 -// Serving tea to table# 2 -// Serving tea to table# 5 -``` - -## 🎱 Proxy - -Real world example - -> Have you ever used an access card to go through a door? There are multiple options to open that door i.e. it can be opened either using access card or by pressing a button that bypasses the security. The door's main functionality is to open but there is a proxy added on top of it to add some functionality. Let me better explain it using the code example below. - -In plain words - -> Using the proxy pattern, a class represents the functionality of another class. - -Wikipedia says - -> A proxy, in its most general form, is a class functioning as an interface to something else. A proxy is a wrapper or agent object that is being called by the client to access the real serving object behind the scenes. Use of the proxy can simply be forwarding to the real object, or can provide additional logic. In the proxy extra functionality can be provided, for example caching when operations on the real object are resource intensive, or checking preconditions before operations on the real object are invoked. - -**Programmatic Example** - -Taking our security door example from above. Firstly we have the door interface and an implementation of door - -```php -interface Door -{ - public function open(); - public function close(); -} - -class LabDoor implements Door -{ - public function open() - { - echo "Opening lab door"; - } - - public function close() - { - echo "Closing the lab door"; - } -} -``` - -Then we have a proxy to secure any doors that we want - -```php -class SecuredDoor -{ - protected $door; - - public function __construct(Door $door) - { - $this->door = $door; - } - - public function open($password) - { - if ($this->authenticate($password)) { - $this->door->open(); - } else { - echo "Big no! It ain't possible."; - } - } - - public function authenticate($password) - { - return $password === '$ecr@t'; - } - - public function close() - { - $this->door->close(); - } -} -``` - -And here is how it can be used - -```php -$door = new SecuredDoor(new LabDoor()); -$door->open('invalid'); // Big no! It ain't possible. - -$door->open('$ecr@t'); // Opening lab door -$door->close(); // Closing lab door -``` - -Yet another example would be some sort of data-mapper implementation. For example, I recently made an ODM (Object Data Mapper) for MongoDB using this pattern where I wrote a proxy around mongo classes while utilizing the magic method `__call()`. All the method calls were proxied to the original mongo class and result retrieved was returned as it is but in case of `find` or `findOne` data was mapped to the required class objects and the object was returned instead of `Cursor`. - -## Behavioral Design Patterns - -In plain words - -> It is concerned with assignment of responsibilities between the objects. What makes them different from structural patterns is they don't just specify the structure but also outline the patterns for message passing/communication between them. Or in other words, they assist in answering "How to run a behavior in software component?" - -Wikipedia says - -> In software engineering, behavioral design patterns are design patterns that identify common communication patterns between objects and realize these patterns. By doing so, these patterns increase flexibility in carrying out this communication. - -There are 10 types of behavioral design patterns - -- [Chain of Responsibility](#-chain-of-responsibility) -- [Command](#-command) -- [Iterator](#-iterator) -- [Mediator](#-mediator) -- [Memento](#-memento) -- [Observer](#-observer) -- [Visitor](#-visitor) -- [Strategy](#-strategy) -- [State](#-state) -- [Template Method](#-template-method) - -## 🔗 Chain of Responsibility - -Real world example - -> For example, you have three payment methods (`A`, `B` and `C`) setup in your account; each having a different amount in it. `A` has 100 USD, `B` has 300 USD and `C` having 1000 USD and the preference for payments is chosen as `A` then `B` then `C`. You try to purchase something that is worth 210 USD. Using Chain of Responsibility, first of all account `A` will be checked if it can make the purchase, if yes purchase will be made and the chain will be broken. If not, request will move forward to account `B` checking for amount if yes chain will be broken otherwise the request will keep forwarding till it finds the suitable handler. Here `A`, `B` and `C` are links of the chain and the whole phenomenon is Chain of Responsibility. - -In plain words - -> It helps building a chain of objects. Request enters from one end and keeps going from object to object till it finds the suitable handler. - -Wikipedia says - -> In object-oriented design, the chain-of-responsibility pattern is a design pattern consisting of a source of command objects and a series of processing objects. Each processing object contains logic that defines the types of command objects that it can handle; the rest are passed to the next processing object in the chain. - -**Programmatic Example** - -Translating our account example above. First of all we have a base account having the logic for chaining the accounts together and some accounts - -```php -abstract class Account -{ - protected $successor; - protected $balance; - - public function setNext(Account $account) - { - $this->successor = $account; - } - - public function pay(float $amountToPay) - { - if ($this->canPay($amountToPay)) { - echo sprintf('Paid %s using %s' . PHP_EOL, $amountToPay, get_called_class()); - } elseif ($this->successor) { - echo sprintf('Cannot pay using %s. Proceeding ..' . PHP_EOL, get_called_class()); - $this->successor->pay($amountToPay); - } else { - throw new Exception('None of the accounts have enough balance'); - } - } - - public function canPay($amount): bool - { - return $this->balance >= $amount; - } -} - -class Bank extends Account -{ - protected $balance; - - public function __construct(float $balance) - { - $this->balance = $balance; - } -} - -class Paypal extends Account -{ - protected $balance; - - public function __construct(float $balance) - { - $this->balance = $balance; - } -} - -class Bitcoin extends Account -{ - protected $balance; - - public function __construct(float $balance) - { - $this->balance = $balance; - } -} -``` - -Now let's prepare the chain using the links defined above (i.e. Bank, Paypal, Bitcoin) - -```php -// Let's prepare a chain like below -// $bank->$paypal->$bitcoin -// -// First priority bank -// If bank can't pay then paypal -// If paypal can't pay then bit coin - -$bank = new Bank(100); // Bank with balance 100 -$paypal = new Paypal(200); // Paypal with balance 200 -$bitcoin = new Bitcoin(300); // Bitcoin with balance 300 - -$bank->setNext($paypal); -$paypal->setNext($bitcoin); - -// Let's try to pay using the first priority i.e. bank -$bank->pay(259); - -// Output will be -// ============== -// Cannot pay using bank. Proceeding .. -// Cannot pay using paypal. Proceeding ..: -// Paid 259 using Bitcoin! -``` - -## 👮 Command - -Real world example - -> A generic example would be you ordering food at a restaurant. You (i.e. `Client`) ask the waiter (i.e. `Invoker`) to bring some food (i.e. `Command`) and waiter simply forwards the request to Chef (i.e. `Receiver`) who has the knowledge of what and how to cook. -> Another example would be you (i.e. `Client`) switching on (i.e. `Command`) the television (i.e. `Receiver`) using a remote control (`Invoker`). - -In plain words - -> Allows you to encapsulate actions in objects. The key idea behind this pattern is to provide the means to decouple client from receiver. - -Wikipedia says - -> In object-oriented programming, the command pattern is a behavioral design pattern in which an object is used to encapsulate all information needed to perform an action or trigger an event at a later time. This information includes the method name, the object that owns the method and values for the method parameters. - -**Programmatic Example** - -First of all we have the receiver that has the implementation of every action that could be performed - -```php -// Receiver -class Bulb -{ - public function turnOn() - { - echo "Bulb has been lit"; - } - - public function turnOff() - { - echo "Darkness!"; - } -} -``` - -then we have an interface that each of the commands are going to implement and then we have a set of commands - -```php -interface Command -{ - public function execute(); - public function undo(); - public function redo(); -} - -// Command -class TurnOn implements Command -{ - protected $bulb; - - public function __construct(Bulb $bulb) - { - $this->bulb = $bulb; - } - - public function execute() - { - $this->bulb->turnOn(); - } - - public function undo() - { - $this->bulb->turnOff(); - } - - public function redo() - { - $this->execute(); - } -} - -class TurnOff implements Command -{ - protected $bulb; - - public function __construct(Bulb $bulb) - { - $this->bulb = $bulb; - } - - public function execute() - { - $this->bulb->turnOff(); - } - - public function undo() - { - $this->bulb->turnOn(); - } - - public function redo() - { - $this->execute(); - } -} -``` - -Then we have an `Invoker` with whom the client will interact to process any commands - -```php -// Invoker -class RemoteControl -{ - public function submit(Command $command) - { - $command->execute(); - } -} -``` - -Finally let's see how we can use it in our client - -```php -$bulb = new Bulb(); - -$turnOn = new TurnOn($bulb); -$turnOff = new TurnOff($bulb); - -$remote = new RemoteControl(); -$remote->submit($turnOn); // Bulb has been lit! -$remote->submit($turnOff); // Darkness! -``` - -Command pattern can also be used to implement a transaction based system. Where you keep maintaining the history of commands as soon as you execute them. If the final command is successfully executed, all good otherwise just iterate through the history and keep executing the `undo` on all the executed commands. - -## ➿ Iterator - -Real world example - -> An old radio set will be a good example of iterator, where user could start at some channel and then use next or previous buttons to go through the respective channels. Or take an example of MP3 player or a TV set where you could press the next and previous buttons to go through the consecutive channels or in other words they all provide an interface to iterate through the respective channels, songs or radio stations. - -In plain words - -> It presents a way to access the elements of an object without exposing the underlying presentation. - -Wikipedia says - -> In object-oriented programming, the iterator pattern is a design pattern in which an iterator is used to traverse a container and access the container's elements. The iterator pattern decouples algorithms from containers; in some cases, algorithms are necessarily container-specific and thus cannot be decoupled. - -**Programmatic example** - -In PHP it is quite easy to implement using SPL (Standard PHP Library). Translating our radio stations example from above. First of all we have `RadioStation` - -```php -class RadioStation -{ - protected $frequency; - - public function __construct(float $frequency) - { - $this->frequency = $frequency; - } - - public function getFrequency(): float - { - return $this->frequency; - } -} -``` - -Then we have our iterator - -```php -use Countable; -use Iterator; - -class StationList implements Countable, Iterator -{ - /** @var RadioStation[] $stations */ - protected $stations = []; - - /** @var int $counter */ - protected $counter; - - public function addStation(RadioStation $station) - { - $this->stations[] = $station; - } - - public function removeStation(RadioStation $toRemove) - { - $toRemoveFrequency = $toRemove->getFrequency(); - $this->stations = array_filter($this->stations, function (RadioStation $station) use ($toRemoveFrequency) { - return $station->getFrequency() !== $toRemoveFrequency; - }); - } - - public function count(): int - { - return count($this->stations); - } - - public function current(): RadioStation - { - return $this->stations[$this->counter]; - } - - public function key() - { - return $this->counter; - } - - public function next() - { - $this->counter++; - } - - public function rewind() - { - $this->counter = 0; - } - - public function valid(): bool - { - return isset($this->stations[$this->counter]); - } -} -``` - -And then it can be used as - -```php -$stationList = new StationList(); - -$stationList->addStation(new RadioStation(89)); -$stationList->addStation(new RadioStation(101)); -$stationList->addStation(new RadioStation(102)); -$stationList->addStation(new RadioStation(103.2)); - -foreach($stationList as $station) { - echo $station->getFrequency() . PHP_EOL; -} - -$stationList->removeStation(new RadioStation(89)); // Will remove station 89 -``` - -## 👽 Mediator - -Real world example - -> A general example would be when you talk to someone on your mobile phone, there is a network provider sitting between you and them and your conversation goes through it instead of being directly sent. In this case network provider is mediator. - -In plain words - -> Mediator pattern adds a third party object (called mediator) to control the interaction between two objects (called colleagues). It helps reduce the coupling between the classes communicating with each other. Because now they don't need to have the knowledge of each other's implementation. - -Wikipedia says - -> In software engineering, the mediator pattern defines an object that encapsulates how a set of objects interact. This pattern is considered to be a behavioral pattern due to the way it can alter the program's running behavior. - -**Programmatic Example** - -Here is the simplest example of a chat room (i.e. mediator) with users (i.e. colleagues) sending messages to each other. - -First of all, we have the mediator i.e. the chat room - -```php -interface ChatRoomMediator -{ - public function showMessage(User $user, string $message); -} - -// Mediator -class ChatRoom implements ChatRoomMediator -{ - public function showMessage(User $user, string $message) - { - $time = date('M d, y H:i'); - $sender = $user->getName(); - - echo $time . '[' . $sender . ']:' . $message; - } -} -``` - -Then we have our users i.e. colleagues - -```php -class User { - protected $name; - protected $chatMediator; - - public function __construct(string $name, ChatRoomMediator $chatMediator) { - $this->name = $name; - $this->chatMediator = $chatMediator; - } - - public function getName() { - return $this->name; - } - - public function send($message) { - $this->chatMediator->showMessage($this, $message); - } -} -``` - -And the usage - -```php -$mediator = new ChatRoom(); - -$john = new User('John Doe', $mediator); -$jane = new User('Jane Doe', $mediator); - -$john->send('Hi there!'); -$jane->send('Hey!'); - -// Output will be -// Feb 14, 10:58 [John]: Hi there! -// Feb 14, 10:58 [Jane]: Hey! -``` - -## 💾 Memento - -Real world example - -> Take the example of calculator (i.e. originator), where whenever you perform some calculation the last calculation is saved in memory (i.e. memento) so that you can get back to it and maybe get it restored using some action buttons (i.e. caretaker). - -In plain words - -> Memento pattern is about capturing and storing the current state of an object in a manner that it can be restored later on in a smooth manner. - -Wikipedia says - -> The memento pattern is a software design pattern that provides the ability to restore an object to its previous state (undo via rollback). - -Usually useful when you need to provide some sort of undo functionality. - -**Programmatic Example** - -Lets take an example of text editor which keeps saving the state from time to time and that you can restore if you want. - -First of all we have our memento object that will be able to hold the editor state - -```php -class EditorMemento -{ - protected $content; - - public function __construct(string $content) - { - $this->content = $content; - } - - public function getContent() - { - return $this->content; - } -} -``` - -Then we have our editor i.e. originator that is going to use memento object - -```php -class Editor -{ - protected $content = ''; - - public function type(string $words) - { - $this->content = $this->content . ' ' . $words; - } - - public function getContent() - { - return $this->content; - } - - public function save() - { - return new EditorMemento($this->content); - } - - public function restore(EditorMemento $memento) - { - $this->content = $memento->getContent(); - } -} -``` - -And then it can be used as - -```php -$editor = new Editor(); - -// Type some stuff -$editor->type('This is the first sentence.'); -$editor->type('This is second.'); - -// Save the state to restore to : This is the first sentence. This is second. -$saved = $editor->save(); - -// Type some more -$editor->type('And this is third.'); - -// Output: Content before Saving -echo $editor->getContent(); // This is the first sentence. This is second. And this is third. - -// Restoring to last saved state -$editor->restore($saved); - -$editor->getContent(); // This is the first sentence. This is second. -``` - -## 😎 Observer - -Real world example - -> A good example would be the job seekers where they subscribe to some job posting site and they are notified whenever there is a matching job opportunity. - -In plain words - -> Defines a dependency between objects so that whenever an object changes its state, all its dependents are notified. - -Wikipedia says - -> The observer pattern is a software design pattern in which an object, called the subject, maintains a list of its dependents, called observers, and notifies them automatically of any state changes, usually by calling one of their methods. - -**Programmatic example** - -Translating our example from above. First of all we have job seekers that need to be notified for a job posting - -```php -class JobPost -{ - protected $title; - - public function __construct(string $title) - { - $this->title = $title; - } - - public function getTitle() - { - return $this->title; - } -} - -class JobSeeker implements Observer -{ - protected $name; - - public function __construct(string $name) - { - $this->name = $name; - } - - public function onJobPosted(JobPost $job) - { - // Do something with the job posting - echo 'Hi ' . $this->name . '! New job posted: '. $job->getTitle(); - } -} -``` - -Then we have our job postings to which the job seekers will subscribe - -```php -class EmploymentAgency implements Observable -{ - protected $observers = []; - - protected function notify(JobPost $jobPosting) - { - foreach ($this->observers as $observer) { - $observer->onJobPosted($jobPosting); - } - } - - public function attach(Observer $observer) - { - $this->observers[] = $observer; - } - - public function addJob(JobPost $jobPosting) - { - $this->notify($jobPosting); - } -} -``` - -Then it can be used as - -```php -// Create subscribers -$johnDoe = new JobSeeker('John Doe'); -$janeDoe = new JobSeeker('Jane Doe'); - -// Create publisher and attach subscribers -$jobPostings = new EmploymentAgency(); -$jobPostings->attach($johnDoe); -$jobPostings->attach($janeDoe); - -// Add a new job and see if subscribers get notified -$jobPostings->addJob(new JobPost('Software Engineer')); - -// Output -// Hi John Doe! New job posted: Software Engineer -// Hi Jane Doe! New job posted: Software Engineer -``` - -## 🏃 Visitor - -Real world example - -> Consider someone visiting Dubai. They just need a way (i.e. visa) to enter Dubai. After arrival, they can come and visit any place in Dubai on their own without having to ask for permission or to do some leg work in order to visit any place here; just let them know of a place and they can visit it. Visitor pattern lets you do just that, it helps you add places to visit so that they can visit as much as they can without having to do any legwork. - -In plain words - -> Visitor pattern lets you add further operations to objects without having to modify them. - -Wikipedia says - -> In object-oriented programming and software engineering, the visitor design pattern is a way of separating an algorithm from an object structure on which it operates. A practical result of this separation is the ability to add new operations to existing object structures without modifying those structures. It is one way to follow the open/closed principle. - -**Programmatic example** - -Let's take an example of a zoo simulation where we have several different kinds of animals and we have to make them Sound. Let's translate this using visitor pattern - -```php -// Visitee -interface Animal -{ - public function accept(AnimalOperation $operation); -} - -// Visitor -interface AnimalOperation -{ - public function visitMonkey(Monkey $monkey); - public function visitLion(Lion $lion); - public function visitDolphin(Dolphin $dolphin); -} -``` - -Then we have our implementations for the animals - -```php -class Monkey implements Animal -{ - public function shout() - { - echo 'Ooh oo aa aa!'; - } - - public function accept(AnimalOperation $operation) - { - $operation->visitMonkey($this); - } -} - -class Lion implements Animal -{ - public function roar() - { - echo 'Roaaar!'; - } - - public function accept(AnimalOperation $operation) - { - $operation->visitLion($this); - } -} - -class Dolphin implements Animal -{ - public function speak() - { - echo 'Tuut tuttu tuutt!'; - } - - public function accept(AnimalOperation $operation) - { - $operation->visitDolphin($this); - } -} -``` - -Let's implement our visitor - -```php -class Speak implements AnimalOperation -{ - public function visitMonkey(Monkey $monkey) - { - $monkey->shout(); - } - - public function visitLion(Lion $lion) - { - $lion->roar(); - } - - public function visitDolphin(Dolphin $dolphin) - { - $dolphin->speak(); - } -} -``` - -And then it can be used as - -```php -$monkey = new Monkey(); -$lion = new Lion(); -$dolphin = new Dolphin(); - -$speak = new Speak(); - -$monkey->accept($speak); // Ooh oo aa aa! -$lion->accept($speak); // Roaaar! -$dolphin->accept($speak); // Tuut tutt tuutt! -``` - -We could have done this simply by having an inheritance hierarchy for the animals but then we would have to modify the animals whenever we would have to add new actions to animals. But now we will not have to change them. For example, let's say we are asked to add the jump behavior to the animals, we can simply add that by creating a new visitor i.e. - -```php -class Jump implements AnimalOperation -{ - public function visitMonkey(Monkey $monkey) - { - echo 'Jumped 20 feet high! on to the tree!'; - } - - public function visitLion(Lion $lion) - { - echo 'Jumped 7 feet! Back on the ground!'; - } - - public function visitDolphin(Dolphin $dolphin) - { - echo 'Walked on water a little and disappeared'; - } -} -``` - -And for the usage - -```php -$jump = new Jump(); - -$monkey->accept($speak); // Ooh oo aa aa! -$monkey->accept($jump); // Jumped 20 feet high! on to the tree! - -$lion->accept($speak); // Roaaar! -$lion->accept($jump); // Jumped 7 feet! Back on the ground! - -$dolphin->accept($speak); // Tuut tutt tuutt! -$dolphin->accept($jump); // Walked on water a little and disappeared -``` - -## 💡 Strategy - -Real world example - -> Consider the example of sorting, we implemented bubble sort but the data started to grow and bubble sort started getting very slow. In order to tackle this we implemented Quick sort. But now although the quick sort algorithm was doing better for large datasets, it was very slow for smaller datasets. In order to handle this we implemented a strategy where for small datasets, bubble sort will be used and for larger, quick sort. - -In plain words - -> Strategy pattern allows you to switch the algorithm or strategy based upon the situation. - -Wikipedia says - -> In computer programming, the strategy pattern (also known as the policy pattern) is a behavioural software design pattern that enables an algorithm's behavior to be selected at runtime. - -**Programmatic example** - -Translating our example from above. First of all we have our strategy interface and different strategy implementations - -```php -interface SortStrategy -{ - public function sort(array $dataset): array; -} - -class BubbleSortStrategy implements SortStrategy -{ - public function sort(array $dataset): array - { - echo "Sorting using bubble sort"; - - // Do sorting - return $dataset; - } -} - -class QuickSortStrategy implements SortStrategy -{ - public function sort(array $dataset): array - { - echo "Sorting using quick sort"; - - // Do sorting - return $dataset; - } -} -``` - -And then we have our client that is going to use any strategy - -```php -class Sorter -{ - protected $sorter; - - public function __construct(SortStrategy $sorter) - { - $this->sorter = $sorter; - } - - public function sort(array $dataset): array - { - return $this->sorter->sort($dataset); - } -} -``` - -And it can be used as - -```php -$dataset = [1, 5, 4, 3, 2, 8]; - -$sorter = new Sorter(new BubbleSortStrategy()); -$sorter->sort($dataset); // Output : Sorting using bubble sort - -$sorter = new Sorter(new QuickSortStrategy()); -$sorter->sort($dataset); // Output : Sorting using quick sort -``` - -## 💢 State - -Real world example - -> Imagine you are using some drawing application, you choose the paint brush to draw. Now the brush changes its behavior based on the selected color i.e. if you have chosen red color it will draw in red, if blue then it will be in blue etc. - -In plain words - -> It lets you change the behavior of a class when the state changes. - -Wikipedia says - -> The state pattern is a behavioral software design pattern that implements a state machine in an object-oriented way. With the state pattern, a state machine is implemented by implementing each individual state as a derived class of the state pattern interface, and implementing state transitions by invoking methods defined by the pattern's superclass. -> The state pattern can be interpreted as a strategy pattern which is able to switch the current strategy through invocations of methods defined in the pattern's interface. - -**Programmatic example** - -Let's take an example of text editor, it lets you change the state of text that is typed i.e. if you have selected bold, it starts writing in bold, if italic then in italics etc. - -First of all we have our state interface and some state implementations - -```php -interface WritingState -{ - public function write(string $words); -} - -class UpperCase implements WritingState -{ - public function write(string $words) - { - echo strtoupper($words); - } -} - -class LowerCase implements WritingState -{ - public function write(string $words) - { - echo strtolower($words); - } -} - -class DefaultText implements WritingState -{ - public function write(string $words) - { - echo $words; - } -} -``` - -Then we have our editor - -```php -class TextEditor -{ - protected $state; - - public function __construct(WritingState $state) - { - $this->state = $state; - } - - public function setState(WritingState $state) - { - $this->state = $state; - } - - public function type(string $words) - { - $this->state->write($words); - } -} -``` - -And then it can be used as - -```php -$editor = new TextEditor(new DefaultText()); - -$editor->type('First line'); - -$editor->setState(new UpperCase()); - -$editor->type('Second line'); -$editor->type('Third line'); - -$editor->setState(new LowerCase()); - -$editor->type('Fourth line'); -$editor->type('Fifth line'); - -// Output: -// First line -// SECOND LINE -// THIRD LINE -// fourth line -// fifth line -``` - -## 📒 Template Method - -Real world example - -> Suppose we are getting some house built. The steps for building might look like -> -> - Prepare the base of house -> - Build the walls -> - Add roof -> - Add other floors - -> The order of these steps could never be changed i.e. you can't build the roof before building the walls etc but each of the steps could be modified for example walls can be made of wood or polyester or stone. - -In plain words - -> Template method defines the skeleton of how a certain algorithm could be performed, but defers the implementation of those steps to the children classes. - -Wikipedia says - -> In software engineering, the template method pattern is a behavioral design pattern that defines the program skeleton of an algorithm in an operation, deferring some steps to subclasses. It lets one redefine certain steps of an algorithm without changing the algorithm's structure. - -**Programmatic Example** - -Imagine we have a build tool that helps us test, lint, build, generate build reports (i.e. code coverage reports, linting report etc) and deploy our app on the test server. - -First of all we have our base class that specifies the skeleton for the build algorithm - -```php -abstract class Builder -{ - - // Template method - final public function build() - { - $this->test(); - $this->lint(); - $this->assemble(); - $this->deploy(); - } - - abstract public function test(); - abstract public function lint(); - abstract public function assemble(); - abstract public function deploy(); -} -``` - -Then we can have our implementations - -```php -class AndroidBuilder extends Builder -{ - public function test() - { - echo 'Running android tests'; - } - - public function lint() - { - echo 'Linting the android code'; - } - - public function assemble() - { - echo 'Assembling the android build'; - } - - public function deploy() - { - echo 'Deploying android build to server'; - } -} - -class IosBuilder extends Builder -{ - public function test() - { - echo 'Running ios tests'; - } - - public function lint() - { - echo 'Linting the ios code'; - } - - public function assemble() - { - echo 'Assembling the ios build'; - } - - public function deploy() - { - echo 'Deploying ios build to server'; - } -} -``` - -And then it can be used as - -```php -$androidBuilder = new AndroidBuilder(); -$androidBuilder->build(); - -// Output: -// Running android tests -// Linting the android code -// Assembling the android build -// Deploying android build to server - -$iosBuilder = new IosBuilder(); -$iosBuilder->build(); - -// Output: -// Running ios tests -// Linting the ios code -// Assembling the ios build -// Deploying ios build to server -``` - -## Wrap Up Folks - -And that about wraps it up. I will continue to improve this, so you might want to watch/star this repository to revisit. Also, I have plans on writing the same about the architectural patterns, stay tuned for it. diff --git a/src/data/guides/devops-automation-tools.md b/src/data/guides/devops-automation-tools.md deleted file mode 100644 index 221d304b3..000000000 --- a/src/data/guides/devops-automation-tools.md +++ /dev/null @@ -1,354 +0,0 @@ ---- -title: '6 Best DevOps Automation Tools: Streamline your workflow' -description: 'Explore the best DevOps automation tools designed to optimize CI/CD, reduce manual tasks, and drive efficiency in your development cycle.' -authorId: fernando -excludedBySlug: '/devops/automation-tools' -seo: - title: '6 Best DevOps Automation Tools: Streamline your workflow' - description: 'Explore the best DevOps automation tools designed to optimize CI/CD, reduce manual tasks, and drive efficiency in your development cycle.' - ogImageUrl: 'https://assets.roadmap.sh/guest/best-devops-automation-tools-aoyls.jpg' -relatedGuidesTitle: 'Other Guides' -relatedGuidesId: devops -isNew: false -type: 'textual' -date: 2024-11-26 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![DevOps Automation Tools](https://assets.roadmap.sh/guest/best-devops-automation-tools-aoyls.jpg) - -[DevOps](https://roadmap.sh/devops) is like the ultimate team-up between development and operations teams, constantly looking to deliver software faster and more reliably. It all boils down to breaking silos, fostering collaboration, and continuously improving the development lifecycle. After all, developers and operations are just two sides of the same coin. - -Now, imagine trying to keep everything running smoothly but doing it manually—sounds like a recipe for chaos, right? [Automation in DevOps](https://roadmap.sh/devops/automation) is crucial because it streamlines workflows and minimizes human intervention. - -In this article, we’ll dive into the **6 best DevOps automation tools** that can help you streamline your workflow and keep your projects on track. Here’s the full list of the tools we’ll be covering: - -1. GitHub Actions -2. Jenkins -3. Ansible -4. Puppet -5. Terraform -6. AWS CloudFormation - -## Criteria for Selecting the Best DevOps Automation Tools - -Choosing the right DevOps automation tools is crucial for optimizing your workflow and ensuring smooth operations. To help you navigate the vast landscape of available options, let's break down the primary categories of automation tools that you'll encounter in the DevOps ecosystem. - -### Key Features to Consider - -Before diving into the actual DevOps automation tools, it's important to keep in mind the key features that make a DevOps tool stand out. - -While we won't delve into the specifics here, remember to consider aspects like: - -- **Integration capabilities**: how well do these tools work with others in the DevOps ecosystem? Considering we’re reviewing automation-related tools, the more and easier they integrate with other systems, the better they’ll be. -- **Scalability**: how much automation can be done through these tools? How easy is it to grow that number? Scalability is a key feature for automation tools, as they’re expected to work unsupervised. -- **User-friendliness**: UX is an important aspect of any tool, especially so for mission-critical systems. The easier they are to set up and maintain, the better. -- **Community support**: an active community around a tool speaks volumes of the quality of that tool. -- **Documentation**: any tool is worthless if it doesn’t have detailed and well-structured documentation. -- **Security features**: understanding how easy these tools make it for the operations team to set up a secure environment will dictate how safe they are (in the context of this article). - -### Categories of Automation Tools - -DevOps automation tools can be grouped into several categories, each addressing different aspects of the development process and operations lifecycle. Understanding these categories will help you identify which tools are best suited for your specific needs. - -#### 1. Continuous Integration/Continuous Deployment (CI/CD) - -CI/CD tools are the backbone of modern DevOps practices. They automate the process of integrating code changes, running tests, and deploying applications. By streamlining these steps, CI/CD tools help ensure that code is consistently built, tested, and released with minimal manual intervention. - -#### 2. Configuration Management - -Configuration management tools focus on maintaining consistency across your infrastructure. They automate the setup, deployment, and management of servers and applications, ensuring that environments remain stable and reproducible. This category is essential for managing large-scale deployments and minimizing configuration drift. - -#### 3. Infrastructure as Code (IaC) - -IaC tools are all about infrastructure management and infrastructure automation. This approach enables version control for your infra, reproducibility for your environments, and scalability, making it easier to provision and manage resources across different environments. - -### My Favorite DevOps Automation Tools - -Alright, and before we go into the details of each tool, let me share my personal favorite ones. - -While all the tools we're about to discuss are fantastic in their own right, there are three that have truly won my nerd heart: **GitHub Actions**, **AWS CloudFormation**, and **Ansible**. - -**GitHub Actions** is like that all-in-one Swiss Army knife you didn’t know you needed—but now you can’t live without it. With a perfect **5/5** in Integration Capabilities, Scalability, User-Friendliness, and Community Support, it checks all the boxes effortlessly. I can’t remember how many times I’ve relied on it for setting up quick CI/CD pipelines with just a few clicks. - -**AWS CloudFormation** is my go-to for managing AWS resources. It scores **5/5** in Integration Capabilities, Scalability, and Security Features, making it indispensable for anyone deeply embedded in the AWS ecosystem. - -Finally, **Ansible** takes the cake when it comes to configuration management and automation. With an outstanding **5/5** ratings across Integration Capabilities, Scalability, User-Friendliness, and Community Support, it’s no wonder Ansible is the go-to tool for so many other DevOps in the industry. - -The following table summarizes the data presented throughout this article: - -![DevOps Automation Tools](https://assets.roadmap.sh/guest/devops-automation-tools-wns3s.png) - -Let’s now take a closer look at all the tools, and let you decide which ones are your favorite ones. - -## Top 6 DevOps Automation Tools - -Now that we've covered the criteria for selecting the best DevOps automation tools, let's dive into our top picks. Each tool is evaluated based on key features from the previous point: integration capabilities, scalability, user-friendliness, community support, and security features. - -Get comfortable, because it’s going to be a long read. - -### 1. GitHub Actions - -![Github Actions](https://assets.roadmap.sh/guest/github-actions-sddcg.png) - -#### Overview - -**GitHub Actions** is GitHub's native continuous integration & continuous deployment (CI/CD) tool, designed to automate your software workflows directly within your GitHub repositories. No matter where in the SDLC (Software Development Lifecycle) you are, GitHub Actions provides a seamless way to integrate these processes into your workflow. - -#### Integration Capabilities 5/5 ★★★★★ - -GitHub Actions shines when it comes to integration. Being part of GitHub, it naturally integrates with your repositories, issues, and pull requests. Plus, with a vast marketplace of pre-built actions, you can easily connect with tools like Slack, Docker, and AWS without breaking a sweat. - -#### Scalability 5/5 ★★★★★ - -Whether you're a solo developer or part of a massive enterprise, GitHub Actions scales effortlessly. Its cloud-based runners can handle anything from small projects to large, complex workflows without skipping a beat. - -#### User-Friendliness 4/5 ★★★★☆ - -While in theory, setting up GitHub Actions should be as easy as pie, in practice, it takes a while to understand their configuration syntax. The YAML syntax and extensive documentation make it accessible (although not trivially simple) even for those new to CI/CD. Plus, the GitHub interface provides a clear view of your workflows, making management a breeze. - -#### Community Support and Documentation 5/5 ★★★★★ - -With GitHub's massive user base, community support is top-notch. You'll find countless tutorials, forums, and GitHub-hosted actions to help you troubleshoot and enhance your workflows. The official documentation is comprehensive and regularly updated. - -#### Security Features 4/5 ★★★★☆ - -GitHub Actions offers robust security features, including encrypted secrets, granular permissions, and support for various authentication methods. While it's secure out of the box, always stay updated with best practices to maintain optimal security. - -### 2. Jenkins - -[![Jenkins CI](https://assets.roadmap.sh/guest/jenkins-3ns0g.png)](https://assets.roadmap.sh/guest/jenkins-3ns0g.png) - -#### Overview - -**Jenkins** is one of the oldest and most popular open-source CI/CD tools in the DevOps landscape. Renowned for its flexibility, Jenkins allows you to automate virtually any part of your development pipeline with its extensive plugin ecosystem. - -#### Integration Capabilities 5/5 ★★★★★ - -Jenkins has an impressive array of plugins that enable integration with almost every tool you can think of—from version control systems like Git and SVN to containerization tools like Docker and Kubernetes. If it exists, there's likely a Jenkins plugin for it. - -#### Scalability 4/5 ★★★★☆ - -Jenkins can handle large-scale projects with ease, especially when set up in a distributed architecture. However, managing numerous plugins and maintaining performance can require a bit more elbow grease compared to some newer tools. - -#### User-Friendliness 3/5 ★★★☆☆ - -Jenkins offers immense flexibility, but this comes at the cost of a steeper learning curve. The user interface is functional but not the most intuitive, and setting up complex pipelines may require a good grasp of Jenkinsfile syntax. In other words, from the UX perspective, Jenkins tends to be geared towards advanced users. - -#### Community Support and Documentation 5/5 ★★★★★ - -With decades in the game, Jenkins has a vast and active community. You'll find extensive documentation, countless plugins, and numerous forums and tutorials to help you navigate any challenges. - -#### Security Features 4/5 ★★★★☆ - -Jenkins provides essential security features like role-based access control, credentials management, and support for various authentication protocols. Regular updates and a proactive community help keep security tight. - -### 3. Ansible - -[![Ansible](https://assets.roadmap.sh/guest/ansible-jxqvt.png)](https://assets.roadmap.sh/guest/ansible-jxqvt.png) - -#### Overview - -**Ansible** is a powerful automation tool focused on configuration management, application deployment, and task automation. It's agentless architecture and simple syntax makes it a favorite among DevOps professionals. - -#### Integration Capabilities 5/5 ★★★★★ - -Ansible integrates seamlessly with a wide range of tools and platforms, including cloud providers like AWS, Azure, and Google Cloud, as well as container platforms like Docker and Kubernetes. Its modular design ensures compatibility with most environments. - -#### Scalability 5/5 ★★★★★ - -Ansible scales effortlessly from managing a handful of servers to thousands. Its push-based model simplifies scaling, allowing you to manage large infrastructures without the need for additional agents. You could say it was designed to scale. - -#### User-Friendliness 5/5 ★★★★★ - -Ansible is known for its simplicity. The YAML-based playbooks are easy to write and understand, making them accessible even for those new to automation. The learning curve is gentle, and the clear documentation supports smooth adoption. - -#### Community Support and Documentation 5/5 ★★★★★ - -Ansible has a vibrant community and extensive documentation. From official guides to community-contributed modules and roles, you'll find plenty of resources to help you understand Ansible and troubleshoot any issues. - -#### Security Features 4/5 ★★★★☆ - -Ansible includes several security features, such as encrypted secrets with Ansible Vault, SSH-based communication, and support for various authentication methods. While it's secure by default, you can never be 100% sure, especially with tools that handle the lowest level of our infrastructure, so keep an eye out for best practices as well. - -### 4. Puppet - -[![](https://assets.roadmap.sh/guest/puppet-cq4m0.png)](https://assets.roadmap.sh/guest/puppet-cq4m0.png) - -#### Overview - -**Puppet** is a robust configuration management tool designed to automate the provisioning, configuration, and management of infrastructure. It's particularly well-suited for large and complex environments. - -#### Integration Capabilities 4/5 ★★★★☆ - -Puppet integrates well with a variety of platforms and tools, including major cloud providers, container platforms, and monitoring tools. Its API and plugin ecosystem extend its compatibility, though it may not be as extensive as some competitors. - -#### Scalability 5/5 ★★★★★ - -Puppet excels in scalability, making it ideal for managing really large infrastructures across multiple environments. Its client-server architecture and efficient resource management ensure smooth operations even at scale. - -#### User-Friendliness 4/5 ★★★★☆ - -Puppet offers a user-friendly experience with its declarative language and comprehensive toolset. However, mastering Puppet's intricacies can take some time, especially for those new to configuration management. Much like Jenkins in its category, you could say Puppet is the choice for advanced users. - -#### Community Support and Documentation 4/5 ★★★★☆ - -Puppet has a strong community and extensive documentation, including modules, tutorials, and forums. While not as large as some other communities, the available resources are highly valuable and well-maintained. - -#### Security Features 4/5 ★★★★☆ - -Puppet provides solid security features, including encrypted data handling, role-based access control, and compliance reporting. Regular updates and a focus on security best practices help maintain a secure environment. - -### 5. Terraform - -[![](https://assets.roadmap.sh/guest/terraform-lapz6.png)](https://assets.roadmap.sh/guest/terraform-lapz6.png) - -#### Overview - -If you’re talking about DevOps automation tools, Terraform is definitely up there on the list. **Terraform** by HashiCorp is a leading Infrastructure as Code (IaC) tool that enables you to define and provision infrastructure across various cloud providers using a declarative configuration language. - -#### Integration Capabilities 5/5 ★★★★★ - -Terraform's extensive provider ecosystem allows it to integrate with almost any cloud service, platform, or tool you can think of. From AWS, Azure, and Google Cloud to Kubernetes, Docker, and even SaaS applications, Terraform is everywhere. - -#### Scalability 5/5 ★★★★★ - -Terraform scales beautifully, managing everything from small projects to large, multi-cloud infrastructures. Its modular approach and state management ensure that scaling up doesn't introduce complexity or instability. - -#### User-Friendliness 4/5 ★★★★☆ - -Terraform offers a straightforward syntax with HashiCorp Configuration Language (HCL), making it relatively easy to learn and use. While powerful, some advanced features may require a deeper understanding, but overall it's user-friendly. - -#### Community Support and Documentation 5/5 ★★★★★ - -Terraform boasts a vibrant community and comprehensive documentation. With countless modules available in the Terraform Registry, along with tutorials and active forums, finding support and resources is a breeze. - -#### Security Features 4/5 ★★★★☆ - -Terraform emphasizes security with features like state encryption, integration with secret management tools, and support for various authentication methods. Proper configuration and adherence to best practices ensure a secure infrastructure. - -### 6. AWS CloudFormation - -[![](https://assets.roadmap.sh/guest/cloudformation-cd374.png)](https://assets.roadmap.sh/guest/cloudformation-cd374.png) - -#### Overview - -**AWS CloudFormation** is Amazon Web Services' native IaC tool, allowing you to model and set up your AWS cloud infrastructure using templates written in JSON or YAML. It's a go-to choice for those deeply embedded in the AWS ecosystem. - -#### Integration Capabilities 5/5 ★★★★★ - -CloudFormation integrates seamlessly with all AWS services, providing a unified experience for managing your AWS infrastructure. While it's primarily AWS-focused, it can also work with third-party services through custom resources. - -#### Scalability 5/5 ★★★★★ - -Designed to handle the full scale of AWS environments, CloudFormation effortlessly manages everything from single instances to complex multi-region architectures. Its stack management ensures that scaling is both efficient and reliable. - -#### User-Friendliness 4/5 ★★★★☆ - -CloudFormation offers a powerful way to manage AWS resources, but the complexity of some templates can be daunting. The AWS Management Console provides a user-friendly interface, and tools like the AWS CLI and SDKs enhance usability. - -#### Community Support and Documentation 4/5 ★★★★☆ - -AWS provides extensive documentation and a solid support ecosystem for CloudFormation. While the community is strong, it’s more niche compared to multi-cloud tools like Terraform. Nevertheless, AWS forums and third-party resources are quite helpful. - -#### Security Features 5/5 ★★★★★ - -Security is a strong suit for CloudFormation, with deep integration with AWS Identity and Access Management (IAM), support for encrypted parameters, and compliance with numerous industry standards. AWS’s robust security infrastructure further enhances CloudFormation's security capabilities. - -## How to Choose the Right DevOps Automation Tool for Your Workflow - -Selecting the perfect DevOps automation tool can feel like a daunting task, especially if your decision will affect many others. Maybe you’re picking the DevOps automation tools for your team or maybe you’re the one deciding for the standards of the company. - -Truth is, when choosing a DevOps automation tool there are some essential factors to consider that have to align with your workflow and organizational needs. If that happens, then you can sleep comfortably at night, knowing you made the best possible choice for your specific context. - -Let’s take a look. - -### Assessing Your Team’s Needs - -Before diving headfirst into the sea of DevOps automation tools, it’s crucial to take a step back and assess what your team truly needs. It might seem obvious, but you’d be surprised how many times a choice is made out of familiarity with a tool rather than by taking into consideration the actual needs of the project. - -Understanding your project requirements and team size will set the foundation for making an informed choice. - -#### 1. Project Requirements - -- **Type of Projects:** Are you working on small, agile projects or large-scale, enterprise-level applications? Different tools excel in different scenarios. -- **Development Practices:** Do you follow specific methodologies like Agile, Scrum, or Kanban? Ensure the tool supports your preferred workflow. -- **Deployment Frequency:** High-frequency deployments require robust CI/CD tools like GitHub Actions or Jenkins to keep up with the pace. -- **Timeline**: Do you have the time to learn a new skill? Is it better to go with something that gets the job done and is familiar to you vs. picking the ideal tool which has a steeper learning curve and will require you to spend some months fiddling around and making educated guesses. - -#### 2. Team Size and Expertise - -- **Skill Level:** If your team is new to DevOps, tools with a gentle learning curve like Ansible or GitHub Actions might be more suitable. -- **Resource Availability:** Larger teams might benefit from more comprehensive tools with extensive features, while smaller teams might prefer lightweight solutions to avoid overcomplicating things. -- **Collaboration Needs:** Consider how the tool facilitates teamwork. Features like shared dashboards and integrated communication can enhance collaboration. - -### Considering Integration with Existing Tools - -No one likes playing nice with others? Well, in the DevOps world, seamless integration is a must. Ensuring compatibility with your current tech stack can save you a lot of time and headaches down the road. - -#### 1. Compatibility with Existing Tools - -- **Version Control Systems:** Ensure the automation tool integrates smoothly with your version control system, whether it’s GitHub, GitLab, or Bitbucket. -- **CI/CD Pipelines:** If you’re already using certain CI/CD tools, check if your new tool can complement or replace them without causing disruptions. -- **Containerization and Orchestration:** Tools like Docker and Kubernetes are staples in many DevOps workflows. Make sure your automation tool plays well with these technologies. - -#### 2. APIs and Extensibility - -- **Custom Integrations:** Look for tools that offer robust APIs, allowing you to create custom integrations tailored to your specific needs. -- **Plugin Ecosystems:** Tools with extensive plugin libraries (like Jenkins) offer greater flexibility and the ability to extend functionality as needed. - -### Evaluating Scalability and Flexibility - -Your organization might be small now, but you’re playing the long game, and who knows what the future holds\! Choosing DevOps automation tools that can scale and adapt as your company grows is essential for long-term success. - -#### 1. Scalability - -- **Handling Increased Load:** Ensure the tool can manage an increase in projects, users, and deployments without performance degradation. -- **Multi-Project Support:** If you plan to expand your project portfolio, choose tools that can handle multiple projects efficiently. - -#### 2. Flexibility - -- **Customization Options:** The ability to customize workflows, scripts, and configurations ensures the tool can adapt to your unique processes. This will make the tool useful in more situations/projects, giving you a higher ROI. -- **Support for Multiple Environments:** Tools that support various environments (development, staging, production) offer greater flexibility in managing different stages of your projects. - -### Budget Considerations - -Let’s face it—budget constraints are a reality for most organizations. Balancing the features you need with what you can afford sometimes feels like a balancing act. - -#### 1. Cost vs. Features - -- **Free vs. Paid Plans:** Many DevOps tools offer free tiers with basic features, which can be great for small teams or startups. Evaluate whether the paid features are worth the investment for your needs. -- **License Models:** Understand the pricing structure—whether it’s based on the number of users, projects, or usage levels. Choose a model that aligns with your usage patterns. - -#### 2. Total Cost of Ownership (TCO) - -- **Initial Setup Costs:** Consider the time and resources required to implement and configure the tool. -- **Maintenance and Support:** Factor in ongoing costs related to updates, support, and potential training for your team. -- **Hidden Costs:** Watch out for additional costs like premium plugins, integrations, or storage fees that might not be apparent upfront. - -### Quick Tips for Choosing the Right Tool - -With all of that said, you might still have some doubts about specific areas where multiple DevOps tools seem equivalent to each other, making it really hard to decide one over the other. At this stage, you can use some of the following techniques to help you make the final call: - -1. **List Your Priorities:** Cut through all the fluff and identify which features are non-negotiable and which ones are nice to have. Evaluate if there is only one tool that properly gives you all the must-have ones, and go with that one. -2. **Trial and Error:** Take advantage of free trials or demo versions to test how well the tool fits your workflow before committing. Make a POC, test the tool out. In the end, you might find out that the documentation tells one story, and the actual implementation is very different. -3. **Seek Feedback:** Involve your team in the decision-making process to ensure the tool meets everyone’s needs and preferences. And if your team hasn’t heard of any of the options, check out online forums, or look for reviews (ideally bad ones) to understand what others have to say about them. In some situations, this is much better and faster than gathering all the information and making an informed decision on your own. -4. **Stay Updated:** The DevOps landscape is constantly evolving. Choose tools that are regularly updated and have a strong roadmap for future enhancements. - -## Conclusion - -Throughout this article, we've explored the **6 best DevOps automation tools** that can significantly streamline your workflow. From **GitHub Actions** and **Jenkins** to **Ansible**, **Puppet**, **Terraform**, and **AWS CloudFormation**, each tool offers unique strengths tailored to different aspects of the DevOps lifecycle. - -DevOps automation tools are essential because they **enhance efficiency, help reduce errors** (by taking care of the repetitive tasks instead of us)**, accelerate delivery, improve collaboration, and ensure scalability.** - -In essence, these tools are the backbone of a successful DevOps strategy, enabling teams to deliver high-quality software. - -Adopting many of these **DevOps automation tools** can transform the way your team works, making your processes more efficient, reliable, and scalable. - -If you’re ready to take the next step in your DevOps journey, you should check out our [DevOps Roadmap](https://roadmap.sh/devops) to get started on learning about these powerful automation tools and take your skills to the next level. diff --git a/src/data/guides/devops-automation.md b/src/data/guides/devops-automation.md deleted file mode 100644 index 9d7166fd0..000000000 --- a/src/data/guides/devops-automation.md +++ /dev/null @@ -1,203 +0,0 @@ ---- -title: 'What is DevOps Automation? 8 Best Practices & Advice' -description: 'Streamline your DevOps pipeline! Explore what DevOps automation is and the 8 best practices for seamless delivery.' -authorId: fernando -excludedBySlug: '/devops/automation' -seo: - title: 'What is DevOps Automation? 8 Best Practices & Advice' - description: 'Streamline your DevOps pipeline! Explore what DevOps automation is and the 8 best practices for seamless delivery.' - ogImageUrl: 'https://assets.roadmap.sh/guest/what-is-devops-automation-03k11.jpg' -relatedGuidesTitle: 'Other Guides' -relatedGuidesId: devops -isNew: false -type: 'textual' -date: 2024-11-05 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![What is DevOps Automation?](https://assets.roadmap.sh/guest/what-is-devops-automation-03k11.jpg) - -DevOps Automation is all about using technology to streamline and enhance the processes involved in software development and IT operations. By automating repetitive tasks, teams can focus more on innovation and less on manual work, in other words, making the job fun and interesting while delivering the boring parts at the same time. - -In this article, we'll explore what DevOps Automation really is, its key components, the benefits it offers, and the best [DevOps](https://roadmap.sh/devops) practices you should follow to get the most out of it. - -## What is DevOps Automation? - -DevOps Automation refers to the use of [automation tools](https://roadmap.sh/devops/automation-tools) and scripts to automate the different stages of the DevOps lifecycle and many aspects of the software development life cycle. This includes everything from code integration and application deployment to infrastructure management and monitoring. - -Automation is one of the key methodologies that help you ensure that these processes are, in the end, efficient, consistent, and reliable. - -## Key DevOps Processes to Automate - -The following key DevOps processes are the core of what automation in the DevOps space is: - -* **Continuous Integration/Continuous Deployment (CI/CD):** Automates the process of integrating code changes and deploying them to production. -* **Infrastructure Management:** Uses code to manage and provision infrastructure, ensuring consistency across environments. -* **Monitoring:** Automatically tracks system performance and alerts DevOps teams to any issues. - -## Components of DevOps Automation - -DevOps Automation isn't just a single tool or process—it's a collection of various components that work together to streamline and enhance your development and operations workflows. Let's dive deeper into these key components to understand how they contribute to a robust DevOps strategy. - -### 1. Continuous Integration (CI) - -![Continuous Integration](https://assets.roadmap.sh/guest/continuous-integration1-cw7ms.png) - -Continuous Integration (CI) is all about integrating code changes into a shared repository frequently, often multiple times a day. This practice helps detect issues early in the development cycle, making it easier to address them before they become bigger problems. - -* **How It Works:** Developers commit their code changes to a central repository. Automated tools then build the application and run a suite of tests (like unit tests and integration tests) to ensure that the new code doesn't break existing functionality. -* **Benefits:** - * **Early Detection of Bugs:** By integrating and testing code regularly, teams can identify and fix bugs quickly. - * **Reduced Integration Problems:** Frequent integrations minimize the complexity and effort required to merge changes from different developers. - * **Improved Code Quality:** Automated testing ensures that code meets quality standards before it's merged. - -### 2. Continuous Deployment (CD) - -![Continuous Deployment](https://assets.roadmap.sh/guest/continuous-deployment1-xqyc2.png) - -Continuous Deployment (CD) takes CI a step further by automatically deploying code changes to production once they pass all the necessary tests. - -* **How It Works:** After the CI process successfully builds and tests the code, the CD pipeline automatically deploys the changes to the live environment without manual intervention. -* **Benefits:** - * **Faster Time-to-Market:** With the automation in place, you significantly reduce the time it takes to deploy changes into production, thus keeping your product competitive. - * **Reduced Risk of Deployments:** Automated deployments minimize human errors, ensuring that deployments are consistent and reliable. - * **Continuous Feedback:** Rapid deployments allow for quicker feedback from users, enabling faster iterations and improvements. - -### 3. Infrastructure as Code (IaC) - -![Infrastructure as Code](https://assets.roadmap.sh/guest/infrastructure-as-code1-ly5zn.png) - -Infrastructure as Code (IaC) is the practice of managing and provisioning your IT infrastructure through machine-readable code rather than manual processes. - -* **How It Works:** Automation tools like Terraform, Ansible, or AWS CloudFormation allow you to define your infrastructure (servers, databases, networks, etc.) in code. This code can be stored in version control systems, reviewed, and reused across different environments. -* **Benefits:** - * **Consistency Across Environments:** Ensures that development, testing, and production environments are identical, reducing the so-called "it works on my machine" syndrome. - * **Versioning your infrastructure:** Changes to infrastructure can be tracked, reviewed, and rolled back if necessary, just like application code. - * **Scalability:** Easily scale your infrastructure up or down by modifying the code, making it adaptable to changing needs. - -### 4. Automated Testing - -![Automated Testing](https://assets.roadmap.sh/guest/automated-test-execution-0uwu9.png) - -Automated testing involves using software tools to run tests on your code automatically. This ensures that your application behaves as expected and maintains high quality as it evolves. - -* **How It Works:** Automated tests (such as unit tests, integration tests, and end-to-end tests) are written alongside your code. These tests are executed automatically during the CI/CD pipeline to verify that new changes don't introduce bugs or regressions. -* **Benefits:** - * **Enhanced Code Quality:** Regular automated testing catches bugs early, ensuring that only reliable code reaches production. - * **Faster Feedback:** Developers receive immediate feedback on their code changes, allowing for quicker iterations and improvements. - * **Efficiency:** Automating repetitive testing tasks saves time and allows teams to focus on more complex testing scenarios and feature development. - -### 5. Monitoring and Logging - -![Monitoring and Logging](https://assets.roadmap.sh/guest/server-monitoring-logging-ye24z.png) - -Monitoring and logging are essential for maintaining the health and performance of your applications and infrastructure. They provide real-time insights and historical data that help you troubleshoot issues and optimize performance. - -* **How It Works:** Monitoring tools continuously track various metrics (like CPU usage, memory consumption, and response times) and logs from your applications and infrastructure. These tools can alert teams to potential issues and even trigger automated responses to certain conditions. -* **Benefits:** - * **Proactive Issue Detection:** Real-time monitoring helps identify and address problems before they impact users. - * **Automated Responses:** Triggering automated actions (like scaling resources during traffic spikes or restarting services after a crash) ensures that your systems remain resilient and performant without manual intervention. - -### Benefits of DevOps Automation - -DevOps automation provides many benefits, including as already mentioned, the side effect of removing humans from almost the entire DevOps workflow, thus reducing the chances of human error and granting DevOps operators more time to focus on more interesting activities. - -However, there are more benefits than just reduced chance of errors. In fact, the following list covers some of the most relevant ones: - -* **Increased Speed and Efficiency:** Automated processes run faster and on time, every time, allowing for faster development and deployment cycles. -* **Enhanced Collaboration Between Teams:** Streamlines workflows, making it easier for development and operations teams to work together. This is, in fact, one of the core [principles of the DevOps practice](https://roadmap.sh/devops/principles). -* **Faster Time-to-Market:** Everything covered so far helps accelerate the delivery of features and updates, keeping your products competitive. - -## Best Practices for DevOps Automation - -![Best Practices for DevOps Automation](https://assets.roadmap.sh/guest/devops-best-practices-rizoz.png) - -### Start with Clear Objectives - -Automation for the sake of automation makes no sense. Before diving into automation, it's essential that you define your goals. - -What are you looking to achieve? Whether it's reducing deployment times, improving code quality, or enhancing collaboration, having clear objectives will guide the rest of your automation strategies and ensure they align with your business goals. - -### Automate Repetitive and Time-Consuming Tasks - -Identifying the repetitive and manual tasks (especially the ones that take a long time to complete) is a crucial next step to understanding what can and should be automated. In fact, these tasks should be the first set of candidates to be automated, as they are directly consuming the time of your teammates with activities that can obviously be done by a machine. - -By automating these tasks, you are effectively freeing up your team to focus on more strategic and creative work, enhancing overall productivity. - -### Implement Continuous Integration and Deployment - -Whenever possible, code changes and the corresponding production deployment should be something that happens automatically with the least amount of human intervention possible. - -This is because it’s such a critical task for any product, that it needs to be 100% reliable and efficient. In other words, it should always work, whether you do it once a week or 5 times on the same day, and it should always be performed as fast as possible. - -This not only speeds up the development process but also ensures that any issues are detected and addressed promptly. CI tools usually offer the option of a rollback, in case something goes wrong. This is another key best practice, as errors will undoubtedly reach production, no matter how much we plan to avoid it, so it’s better to be prepared than to ignore the possibility and then have to manually perform rollbacks, or even worse, apply fixes directly in production. - -### Use Infrastructure as Code (IaC) - -Managing your infrastructure can be a daunting task for projects that have a complex architecture. - -With IaC DevOps operators can manage the infra through code ensuring consistency across all environments. - -Thanks to the “code” part of IaC, you can version control your infrastructure, making it easier to reproduce environments and roll back changes if needed. - -### Ensure Proper Monitoring and Logging - -Implement comprehensive monitoring solutions to keep an eye on your applications and infrastructure is a key DevOps automation practice. - -After all, if you plan to improve, you need to measure, right? - -In the same train of thought, effective logging helps you gain valuable insights and troubleshoot issues in your platform, ensuring your systems run smoothly for as long as possible. - -### Foster a Culture of Collaboration and Continuous Improvement - -Break with the idea that developers and operations should not work together side-by-side, and instead encourage open communication between both teams. - -Promote a culture of continuous learning and adaptation, where teams regularly review and improve their processes based on feedback and new insights. - -### Integrate Security into the Automation Process (DevSecOps) - -Security should be an integral part of your DevOps pipeline and adding automation tools into the mix should not affect that. - -Make sure to embed security practices within your DevOps automation processes and automate security testing and compliance checks to ensure that your applications are secure from the ground up. - -### Address and Eliminate Toil - -Toil refers to repetitive, manual tasks that do not provide lasting value, for instance: performing manual deployments after each sprint. - -Imagine your development team needs to deploy a new version of your web application every week. Each deployment involves several repetitive steps, like logging into the server, copying the files, configuring the server, restarting all services and checking if everything went right. - -While you might consider production deployments to be very valuable, the truth is that if you have to do it every week, then the **lasting** value is non-existent. - -Try to identify sources of toil in your workflows and continuously work to reduce or eliminate them through automation. - -## Choose your DevOps automation tools - -Once you’ve incorporated these best practices into your DevOps activities, the next immediate action is to decide what your top [DevOps automation tools](https://roadmap.sh/devops/automation-tools) will be. -These tools should allow you to: - -* Comply with the best practices already covered. -* Empower your team to accomplish their tasks without getting in their way. -* Actually provide the automation level you require. - -Regardless of the automation tools you decide to go with, some of the recommended categories that you should tackle are: - -* **Continuous Integration/Continuous Deployment** (CI/CD): these will help you reduce your time-to-market and increase the confidence on every new deployment. -* **Configuration Management tools**: incredibly useful when you have to manage large infrastructures. -* **Infrastructure as Code** (IaC): they allow you to version your infrastructure and collaborate on it through simple text files, speeding up resource deployment & environment setups. -* **Test automation tools**: they will help you ensure quality by automating the testing of many aspects of your application (i.e automatically running UI tests as part of your deployment process). - -## Conclusion - -DevOps automation is a powerful approach to streamline your software development and IT operations interactions. - -By automating key portions of your software development process like CI/CD, infrastructure management, and monitoring, you can achieve better speed, consistency, and overall improvement in collaboration within your teams. - -Implementing best practices will ensure your automation efforts are successful and aligned with your business goals. - -If you’re looking for more details on how to get started as a DevOps or you’d like to learn more about this practice, check out our [DevOps roadmap here](https://roadmap.sh/devops). diff --git a/src/data/guides/devops-best-practices.md b/src/data/guides/devops-best-practices.md deleted file mode 100644 index b94648a80..000000000 --- a/src/data/guides/devops-best-practices.md +++ /dev/null @@ -1,265 +0,0 @@ ---- -title: '14 DevOps Best Practices and Methods You Should Know' -description: 'Learn what the essential backend skills you should master to advance in your career.' -authorId: william -excludedBySlug: '/devops/best-practices' -seo: - title: '14 DevOps Best Practices and Methods You Should Know' - description: 'Learn DevOps best practices for building efficient workflows, from code integration to monitoring, and avoid common DevOps anti-patterns.' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-best-practices-4yhmb.jpg' -relatedTitle: "Other Guides" -relatedGuidesId: devops -isNew: false -type: 'textual' -date: 2024-11-26 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![DevOps Best Practices and methods you should know](https://assets.roadmap.sh/guest/devops-best-practices-4yhmb.jpg) - -DevOps is a proven practice and set of standards that has helped small to large enterprises streamline their software development process and IT operations. It has helped break down silos and deliver more reliable and high-quality software. Despite the benefits it brings, its implementation can be difficult. This is because you need to know the process, the [DevOps tools](https://roadmap.sh/devops/tools) to use, and a shift in operational culture, which can be challenging. - -Although every organization approaches these challenges in slightly different ways, there are **best practices** that you can follow to achieve successful implementation. - -In this guide, you’ll learn key DevOps best practices to improve your workflow, and common [DevOps](https://roadmap.sh/devops) anti-patterns to avoid when adopting these practices. - -**TL;DR: 14 DevOps key best practices you should know are:** - -1. Embrace a culture of collaboration and communication. -2. Continuously seek feedback. -3. Adopt continuous integration. -4. Practice continuous delivery. -5. Embrace Infrastructure as Code (IaC). -6. Adopt microservices architecture. -7. Use version control for everything. -8. Practice continuous monitoring and observability. -9. Adopt configuration management. -10. Practice DevSecOps. -11. Choose the right DevOps tools. -12. Balance quality and speed. -13. Use feature flag to manage features. -14. Adopt agile project management. - -## DevOps key best practices - -Here are 14 key best practices and methods for implementing DevOps: - -### 1. Embrace a culture of collaboration and communication - -Collaboration and communication are central to the success of DevOps lifecycle. You need to encourage your team to continuously take ownership of their tasks and communicate (verbal and written) effectively between the development teams, operations teams, quality assurance teams, and other related stakeholders. - -View mistakes and failures as learning opportunities rather than assigning blame. You and your team should hold postmortems to identify the root causes of incidents and develop strategies to prevent future occurrences. - -### 2. Continuously seek feedback - -Continuously engage your users, stakeholders, and team members to get feedback on their overall experience of the application. This feedback helps resolve issues, shape your application release cycle, and guarantee you’re building with users in mind. As noted in the latest [State of DevOps Report](https://cloud.google.com/resources/devops/state-of-devops), “*Teams that focus on the user* *experience* *have 40% higher organizational performance than t**hose* *that don’t*.” - -Internally, your team can also benefit from continuous feedback during code reviews. By involving stakeholders in analyzing code quality, identifying issues, and suggesting improvements, you can create a culture of constant improvement. - -### 3. Adopt continuous integration - -Continuous Integration (CI) is a software development practice in which developers regularly commit their code changes into a shared repository, which might happen multiple times a day. The goal of CI is to provide feedback quickly to developers so that they can identify and fix defects early in the development lifecycle. - -The CI process involves: - -- **Automated builds**: When you commit a code into a shared repository, it triggers an automatic build to verify that the code integrates well with the existing project. -- **Automated testing**: After the build is successful, automated tests are run to validate that the changes committed don’t break the application. - ![CI Process](https://assets.roadmap.sh/guest/ci-process-07zce.png) - -To support your application in following the CI process and delivering the best value, you need to: - -- Commit small and incremental changes to the shared repository often. -- Automate the build process so every commit results in a reliable and repeated build. -- Set up a test to run with every commit to catch any issues as soon as they are introduced. -- Fix broken builds immediately. -- Use a version control system like Git to manage deployment, branches, and track commits. -- Document how the CI process works and how to resolve common issues like broken builds. This will help your team work faster and onboard new members easily. - -### 4. Practice continuous delivery - -Continuous Delivery (CD) is a software engineering practice where code changes are automatically prepared for release to production. It extends the CI process by ensuring that the codebase that has been added, tested, and passed is ready for release into the staging or production environment. - -The goal of CD is to automate the release pipeline so that new features, bug fixes, and improvements can be deployed to production at any time without manual intervention. - -To implement CD effectively into your DevOps processes, you need to: - -- Automate the entire deployment pipeline (from testing to deployment) to minimize manual error, reduce deployment time, and maintain consistency across environments. -- Use Git as a source of truth for development and infrastructure management. -- Track the health of your application by monitoring performance, errors, and logging. -- Set up your pipeline so it can revert to previous working deployments in case of critical error. -- Use feature flags to roll out features gradually into production. -- Integrate security measures into your pipeline to scan and check for vulnerabilities. - -### 5. Embrace Infrastructure as Code (IaC) - -Infrastructure as Code (IaC) is a DevOps practice that uses machine-readable definition files to manage and provision computing infrastructure rather than physical hardware configuration or direct interaction with configuration tools. It involves using code (JSON, XML, or domain-specific languages) to specify and configure providers, resources, and more. - -IaC aims to eliminate manual workflow in provisioning resources, making deployment faster and less prone to human errors. To implement IaC effectively into your DevOps processes, you need to: - -- Use Git to collaborate, track changes over time, and easily roll back to previous changes over version if necessary. -- Break down your infrastructure code into reusable components. This makes it easy to manage and reuse across multiple projects. -- Write unit tests, integration tests, and other security-related tests to validate that IaC works as designed. -- Use environment variables to manage sensitive values like API ket, resource IDs, and more. -- Use continuous integration and continuous deployment (CI/CD) to integrate IaC into your pipeline to automatically provision or update infrastructure. - -The recently concluded [State of DevOps Report](https://cloud.google.com/resources/devops/state-of-devops) also highlights the importance of IaC for its elasticity, on-demand resource provisioning, and flexibility. These advantages have contributed to a 30% higher organizational performance compared to those not utilizing IaC. - -### 6. Adopt microservices architecture - -Microservice architecture involves breaking down your application into small, independent, and deployable services. Each service is responsible for a specific functionality and can be independently developed, tested, and deployed. This architecture promotes scalability, fault isolation, and rapid innovation, which are essential to the DevOps environment. - -![Microservices Architecture](https://assets.roadmap.sh/guest/microservices-architecture-wo59y.png) - -To implement microservice architecture effectively into your DevOps processes, you need to: - -- Design your microservice to have retries, circuit breakers, and fallback mechanisms to handle failure gracefully. -- Build your microservice to manage its database or data source to avoid tight coupling. -- Use containerization and orchestration tools like [Docker](https://roadmap.sh/docker) and [Kubernetes](https://roadmap.sh/kubernetes) to manage deployment. -- Implement CI/CD pipeline to enable reliable and consistent updates to individual services. -- Use message brokers like Kafka and RabbitMQ to communicate between services and reduce dependencies. - -> In my opinion, a microservice architecture is only suitable for mature systems with multiple intertwined parts built for scale rather than a relatively small software that could be run better as a mon - -### 7. Use version control for everything - -Version control is a system that records changes to files over time. It lets you track, manage, and collaborate on code efficiently. To use Git effectively in your DevOps processes, you need to: - -- Use effective branching strategies like feature branching and Git flow to manage the development process. -- Promote frequent commits with descriptive commit messages. -- Use pull requests (PRs) to facilitate code review and collaboration between the development and operations teams. -- Keep your repositories clean of generated files like binaries, logs, or dependencies. -- Use tag releases to mark important milestones, e.g., `v1.0.2`. -- Use descriptive branch names. This will help you and your team understand the purpose of each branch. - -![Version control](https://assets.roadmap.sh/guest/version-control-6i5g0.png) - -### 8. Practice continuous monitoring and observability - -**Monitoring** involves collecting, analyzing, and visualizing data from your system in real-time to detect issues like outages, bottlenecks, or resource exhaustion. On the other hand, **Observability** is about understanding what is happening inside the system by analyzing data like logs, metrics, and traces. To take advantage of monitoring and observability in your DevOps processes, you need to: - -- Define Service Level Agreements (SLAs), Service Level Objectives (SLOs), and other performance-related metrics to clarify performance and ensure proper monitoring coverage. -- Monitor key metrics like uptime, error rates, latency, and CPU usage. -- Use tools like Grafana and Kibana to create real-time visual dashboards that can give you insights on how your application is performing. -- Collect all your internal data (logs, metrics, and traces) in a centralized location for easy access and analysis. - -### 9. Adopt configuration management - -Configuration management is the process of systematically handling changes to a system’s configuration to ensure consistency and integrity. It involves defining and maintaining settings, dependencies, and states of servers, network devices, applications, and databases. The goal is to make sure infrastructure is configured correctly, and changes are managed effectively to avoid errors and downtime. - -To take advantage of configuration management in your DevOps processes, you need to: - -- Automate the entire configuration process, from setting up applications and services to deployment of servers. -- Use Git to manage and store your configurations centrally. This makes it easy to track changes, collaborate, and roll back if needed. -- Write modular configuration files that can be reused across different environments. -- Document all your configurations so that your team members can understand. - -### 10. Practice DevSecOps - -![DevSecOps](https://assets.roadmap.sh/guest/devsecops-rafjn.png) - -DevSecOps stands for **Development, Security, and Operations**. It’s an extension of DevOps that promotes the integration of security practices into every phase of the software development lifecycle (SDLC). In the traditional development process, security is often handled by a separate team at the end of the process. In this case, when vulnerabilities are discovered, it slows down the deployment and increases the cost of fixing issues. - -To prevent such incidents in your DevOps processes, integrate security practices and tools into all stages of your SDLC to avoid downtime and fix vulnerabilities early. - -### 11. Choose the right DevOps tools - -Selecting the right DevOps tools can significantly impact the success of your DevOps processes. The ecosystem is constantly changing, and you’ll get varying opinions on tools to use; it's important to carefully evaluate options. Consider your team’s strengths, the learning curve, and your organization’s goal before adopting any tool for your DevOps pipeline. - -Below are some tools you should consider when implementing DevOps processes: - -- **CI/CD tools**: Tools like Jenkins, GitLab CI/CD, CircleCI, Azure Pipeline, and GitHub Actions help automate the integration and deployment of code changes. -- **Infrastructure as Code (IaC) tools**: Tools like Terraform, Ansible, Pulumi, Chef, AWS CloudFormation, and Vagrant help automate the provisioning and management of infrastructure. -- **Containerization and orchestration tools**: Tools like Docker, Kubernetes, OpenShift, Docker Swarm, and Amazon ECS (Elastic Container Service) help manage and orchestrate containers at scale. -- **Monitoring and logging tools**: Tools like Prometheus, ELK Stack (Elasticsearch, Logstash, and Kibana), Datadog, Splunk, and Grafana help track system performance, logging, and alerting. -- **Configuration management tools**: Tools like Chef, Puppet, CFEngine, SaltStack, and Ansible help ensure system configurations remain consistent across environments. -- **Security and compliance tools**: Tools like HashiCorp Vault, OWASP ZAP, Snyk, SonarQube, and Aqua Security help enforce security policies, perform scans, and conduct compliance checks. -- **Collaboration and communication tools**: Tools like Slack, Microsoft Teams, Trello, Jira, and Confluence help facilitate communication and collaboration between teams. - -### 12. Balance quality and speed - -A key promise of DevOps is that it delivers software quickly. While it's tempting to speed up development processes, this often leads to reduced quality and potential security vulnerabilities. The pressure is further heightened because a lot of metrics in DevOps are based on how fast your system is, making it even more tempting to prioritize fast delivery over quality. - -To avoid these pitfalls, strike a balance between quality and speed by taking the time to test the application before release. Below are some strategies to help maintain this balance: - -- Implement continuous testing into your CI/CD pipeline to catch bugs early. -- Encourage a culture of quality that makes developers take full ownership of their code. -- Define quality checkpoints like code reviews, tests, and coding standards that must be passed before moving to production. - -### 13. Use feature flags to manage features - -Feature flags (also known as Feature Toggles) are powerful tools that let you enable or disable specific features in an application without deploying new code. They provide fine-grain control over the parts of an application that are visible and active at any time. - -![Feature flags](https://assets.roadmap.sh/guest/feature-flags-7unnx.png) - -You can use a feature flag in your DevOps processes to: - -- Perform gradual feature rollout. -- Safely deploy features and rollback if needed. -- Perform A/B testing - -### 14. Adopt agile project management - -Agile software development methodologies like Scrum and Kanban are well-suited to the iterative nature of DevOps. Agile uses a continuous approach to develop software rather than the linear waterfall model. - -![Agile VS Waterfall](https://assets.roadmap.sh/guest/agile-project-management-1pjcq.png) - -By embracing agile project management, you manage your projects better by breaking them down into smaller and manageable chunks rather than dealing with them in large chunks that are prone to error. - -## DevOps anti-patterns to avoid - -While DevOps has become the go-to approach for delivering scalable and robust applications, it has also introduced certain anti-patterns that can prevent you from fully reaping its benefits. These anti-patterns can lead to inefficiencies, limit collaboration, and result in poor outcomes. Let’s look at some common anti-patterns you should avoid: - -**Automating everything without understanding** -Because automation is at the center of DevOps, teams often fall into the trap of automating every process without understanding the underlying workflow or business logic. This can lead to incorrect implementation, inefficient processes, complexities, and sometimes bugs. - -Before you automate a process, make sure you understand the underlying workflow. Start with critical processes that provide the most value, and regularly review automated processes to confirm they are providing value. - -**DevOps as a one-time project** -When most teams build or extend a project, timelines are usually attached. They have start and end dates for product design, engineering, **DevOps**, QA, and more, which may cause them to view DevOps as a short-term project. This prevents teams from realizing the long-term benefits and leads to a lack of continuous improvement. - -You need to adopt DevOps as a continuous practice by regularly evaluating processes and workflow and encouraging your team to experiment. - -**Not having a proper monitoring and feedback loop** -Failing to implement adequate monitoring and feedback mechanisms to track application performance, reliability, and user experience makes it difficult for the DevOps team to detect issues in real-time. This can lead to longer recovery time when there’s an issue, and the team may miss opportunities to learn and improve the system. - -To address this, you need to implement end-to-end monitoring and observability tools to gain insight into the system’s health. Additionally, a clear feedback loop should be established so that responsible teams are notified promptly when issues are detected. - -**Siloed DevOps team** -A common practice in most organizations is to separate the DevOps Team from other teams and task them with all DevOps activities. This separation defeats the core principle of DevOps: cross-functional collaboration and shared ownership. It isolates the DevOps team from others, leading to communication gaps and a lack of ownership. - -To address this, promote a DevOps culture of shared responsibility where DevOps practices are adopted across all teams. - -**Over-reliance on tools** -Adopting the latest and trendiest DevOps tool doesn’t guarantee success. Sometimes, they even increase the problems if not used correctly. DevOps is about processes, people, and tools. - -To address this, follow the right processes: ensure effective team collaboration, improve workflows and align with the teams before choosing tools. - -**Too many feature flags** -While feature flags are powerful, too many can lead to technical debt, making the codebase messy, cluttered, and difficult to maintain. - -To address this, use feature flags as needed and regularly clean up flags that are no longer needed. - -**Overcomplicating the CI/CD pipeline** -When pipelines are overly complicated with multi-step CI/CD, stages, and checks, it leads to slow deployments. This can slow down the releases, become a bottleneck, and make troubleshooting difficult. - -You can address this by keeping the pipeline simple and focusing on key steps like build, test, and deploy. Then, add stages only when needed. - -**Not defining what success looks like** -Failing to define and track meaningful metrics can make it difficult to measure the success or failure of DevOps practices. Without clear metrics, the team may not know if they are improving, and the team leads may struggle to justify the investment in DevOps to management. - -You can address this by defining a key performance index (KPI) that aligns with your business goal, such as deployment frequency, change lead time, and mean time to recovery (MTTR). - -## Key takeaway - -DevOps best practices are essential for improving your software development and operations processes. These practices help improve your workflows, enhance collaboration, manage resources better, and ensure the delivery of high-quality software. - -Additionally, you need to be aware of anti-patterns and pitfalls that can hinder the success of your DevOps implementation by avoiding common patterns like creating silos or overcomplicating your pipelines. - -Stay up-to-date with the latest developments in the DevOps ecosystem by following the [DevOps roadmap](https://roadmap.sh/devops). - diff --git a/src/data/guides/devops-career-path.md b/src/data/guides/devops-career-path.md deleted file mode 100644 index a891ab695..000000000 --- a/src/data/guides/devops-career-path.md +++ /dev/null @@ -1,238 +0,0 @@ ---- -title: 'Is DevOps engineering a good career path in @currentYear@?' -description: 'Learn why a DevOps career path is a smart choice in 2025. Get insights into demand, growth, and earning potential in DevOps.' -authorId: ekene -excludedBySlug: '/devops/career-path' -seo: - title: 'Is DevOps engineering a good career path in @currentYear@?' - description: 'Learn why a DevOps career path is a smart choice in 2025. Get insights into demand, growth, and earning potential in DevOps.' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-engineer-career-path-2h4r7.jpg' -relatedGuidesTitle: 'Other Guides' -relatedGuidesId: devops -isNew: false -type: 'textual' -date: 2024-08-20 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Is it worth learning DevOps as a career path?](https://assets.roadmap.sh/guest/devops-engineer-career-path-2h4r7.jpg) - -Making career choices could be overwhelming for beginners and experienced software developers seeking to advance their skills. This could be due to several factors, such as the abundance of options, the numerous resources on the internet, the steep learning curves, and so on. - -However, before selecting a path, it is helpful to look at certain factors, such as your interests, strengths, and the future prospects of the career path, as these factors play a crucial role in determining your potential for success. - -[DevOps engineering](https://roadmap.sh/devops) is one of the most [in-demand and highest-paying roles](https://uk.indeed.com/career-advice/career-development/software-engineering-jobs) in the tech industry and, in recent times, has become the go-to choice for many people getting into tech and experienced tech professionals. - -As a DevOps professional, you'll enjoy spectacular career growth filled with endless opportunities. - -The DevOps philosophy involves bringing developers and operation teams together to improve the software delivery process. - -This guide will detail DevOps and provide the necessary information to help you decide whether to pursue the Devops engineer career path and steps to ensure a DevOps career growth. - -## What is DevOps? - -Derived from the combination of development (Dev) and operations (Ops), DevOps is a software development methodology that aims to improve collaboration between development and operations teams, increase the efficiency, security, and speed of software development and delivery. - -![DevOps](https://assets.roadmap.sh/guest/devops-8vc2m.jpg) - -Within DevOps, you'll play an important part in the entire software development lifecycle - from initial planning to implementation. This means you'll be a team player with excellent communication skills. - -## Is DevOps engineer career path right for you? - -DevOps is a field that's here to stay. The DevOps market grew to an incredible $10.3 billion at the end of 2023 and it is growing. Hence, securing a DevOps role is your first step toward a long-lasting career. - -DevOps career paths are worth considering if you have experience in software development, networking, or operations. It involves automation, testing, monitoring, configuring, networking, and Infrastructure as Code (IaC) and requires a diverse skill set as discussed below. It is a bridge between development and operations teams. - -These are some factors to consider before choosing the DevOps engineer career path: - -- Interest in automation -- Enjoy collaborating -- Interest in infrastructure management -- Love for problem-solving -- Willingness to continuously learn new skills and technology - -### Interest in automation - -[Automation](https://roadmap.sh/devops/automation) is an integral part of the DevOps career path. It involves writing scripts and code to automate repetitive tasks and enhance software delivery processes. By automating repetitive tasks and workflows, DevOps teams can increase efficiency, reduce errors, and accelerate time to market for software releases. - -### Enjoy collaborating - -Collaboration is crucial in the DevOps career, as you will work with different people across several teams. The goal is to break down the silos across teams and ensure they all work together to achieve the same goal. Having great collaboration skills is crucial to being a DevOps engineer. - -### Interest in infrastructure management - -Do you enjoy working on infrastructural rather than domain code? The plus side of infrastructural code is that it can be replicated across several infrastructures once it is set up, and you can transfer the knowledge to other organizations instead of domain code, where you would always need to learn the domain of the business you are writing code for. - -### Love for problem-solving - -Choosing this field requires that you enjoy solving problems and can devise solutions to complex problems. - -### Willingness to continuously learn new skills and technology - -DevOps is an evolving field, and there is always something new. To be up to date, you have to be willing and open to continuous learning. This involves taking courses, reading articles, and getting updates on things happening in the DevOps field and tech. - -It is worth noting that working in DevOps involves working in high-pressure environments. You are constantly relied on to manage an organization's IT and new and existing cloud systems, which can sometimes be overwhelming. - -Also, there is a steep learning curve. As a tech beginner, it could be daunting and challenging to get into DevOps and adapt the DevOps culture, but as you go along, it gets easier. - -## DevOps in 2025 - -According to [Statista](https://www.statista.com/statistics/1367003/in-demand-it-roles/), DevOps software engineering positions are among the top technical positions demanded by recruiters worldwide in 2023. Indeed reported that the average annual salary of [DevOps engineer](https://www.indeed.com/career/development-operations-engineer/salaries?from=top_sb) in the USA is $124,392. - -DevOps has evolved over the last decade. Today, it is more than automating tasks and having engineers write scripts. It is now more about practices that can help to automate software delivery, improve business and the overall software development processes. - -Certain trends are impacting the DevOps market currently and will also play a role in the future of DevOps. Some of them include: - -- Microservices -- Cloud technology -- Automation and CI/CD -- Artificial Intelligence and Machine Learning - -Let's look at these trends and how they indirectly influence your decision. - -### Microservices - -This architecture enables the agile development and continuous delivery of software solutions. In a microservice architecture, applications are split into smaller parts known as microservices that focus on a single responsibility. Each part (microservice) is developed and deployed independently, and microservices communicate via events or API interfaces. - -It is a common trend, and many organizations are adopting this architecture because of its benefits, one of which is adapting to market changes and shipping out features faster rather than the delays of modular monoliths. A DevOps engineer is critical to helping in the adoption and success of microservices. - -### Cloud technology - -Cloud-native applications have become popular recently. They involve developing and deploying software applications and their dependencies in a cloud environment. - -There are several cloud platforms, some of the most popular ones being [AWS](https://roadmap.sh/aws), Microsoft Azure, and Google Cloud Platform (GCP). One advantage of using these cloud providers is that you don't have to manage the cloud infrastructure but instead focus on developing your applications. You also pay for only the resources you use. - -Containerization tools like [Docker](https://roadmap.sh/docker) and [Kubernetes](https://roadmap.sh/kubernetes) have been made popular by cloud services and microservices. These [tools for DevOps engineers](https://roadmap.sh/devops/tools) are an important part of their toolkit. - -### Automation and CI/CD - -Automation and continuous integration/continuous deployment are integral to DevOps. Organizations are widely adopting automation of their infrastructure and deployments because of its benefits including faster and more reliable application deployments. - -Also, with the adoption of GitOps, an operational framework that takes DevOps principles and best practices for application development and applies them to infrastructure automation, the deployment process is even more efficient. DevOps professionals are the major catalysts for this and will remain relevant. - -### Artificial intelligence and machine learning - -AI and ML have become integrated into our daily lives and [automation tools](https://roadmap.sh/devops/automation-tools) are used to automate processes and routine tasks, monitor system health, and predict potential system issues. These AI tools need to be designed, maintained, and enhanced. - -In the AI and ML field, it is the job of an MLOps engineer, but a DevOps engineer can upskill and switch roles to an MLOps engineer. - -There is a concern that AI will replace DevOps professionals. However, I believe AI will complement the DevOps process, improve the software development lifecycle, and make better DevOps engineers. - -## Specializations in DevOps - -The DevOps career paths are rewarding and successful. The DevOps career also offers a lot of growth opportunities, and as you have seen in the previous section, it is in high demand. - -There are several DevOps career paths and devops career opportunities for both entry-level and experienced positions. Normally, working in DevOps starts with an entry-level position like a release manager or junior DevOps engineer. - -As a DevOps professional, you can decide to go for any of these following DevOps skills and specializations: - -- Automation expert -- General DevOps engineer -- System engineer -- DevOps architect -- DevOps release manager -- DevSecOps engineer -- DevOps test engineer - -### Automation expert - -In the DevOps career path, you can work as an automation expert or engineer, depending on the organization. You can specialize in implementing automation solutions, continuous improvement, and software delivery. As automation plays a critical role, every DevOps engineer should be familiar with the automation process. - -Automation experts specialize in implementing continuous integration (CI) and continuous delivery (CD) within the software lifecycle to boost the efficiency of development and operations teams. Additionally, they design and integrate monitoring, dashboard, and incident management tools like [Grafana](https://grafana.com/), [Loki](https://grafana.com/oss/loki/), and [Seq](https://datalust.co/seq). - -### General DevOps engineer - -This is one of the key DevOps career path. As a DevOps engineer, you work closely with developers and act as a bridge between other team members. You are involved in all aspects of the software development life cycle. You are also a bridge between operations and development teams. DevOps engineers need to be proficient with top DevOps automation tools and have knowledge of cloud platforms like AWS and Google Cloud. Usually a newbie in DevOps starts in this path as a junior DevOps engineer. - -### Systems engineer - -This is another DevOps career you can assume as you become a DevOps engineer. As a system engineer, you are responsible for designing, deploying, and maintaining an organization's IT infrastructure, including the hardware, software, networking, and operating systems. - -### DevOps architect - -In this DevOps career path, a DevOps architect is responsible for designing and implementing the overall DevOps architecture and processes in an organization. - -A DevOps architect is responsible for building the foundation upon which the entire process rests. The DevOps architect role is a more senior role than a DevOps engineer. - -A DevOps architect is like the contractor of the DevOps world and ensures consistency of agile principles across the DevOps process and work closely with other senior DevOps engineers and professionals to ensure these principles are followed. - -### DevOps release manager - -This is a DevOps career path where you are responsible for managing and overseeing software releases throughout the DevOps process. A DevOps release manager ensures software products are released on time, with high quality and reliability. - -### DevSecOps engineer - -DevSecOps stands for Development, Security and Operations. Such engineers design and implement secure architectures for software and infrastructure, manage vulnerabilities, and protect against security threats. - -DevSecOps engineers ensure that software applications and their supporting infrastructure are secure. - -### DevOps test engineer - -A DevOps test engineer is responsible for implementing tests to ensure software products are high-quality, reliable, and scalable. They oversee all stages of the testing process, such as designing automated testing frameworks, identifying and resolving issues, and certifying compliance with industry standards. - -Other DevOps roles include: - -- DevOps Cloud Engineer -- Lead DevOps Engineer - -## Skills required in DevOps - -DevOps engineers require both technical skills and soft skills, which may vary from organization to organization, as well as team structure, technologies, and tools. However, some common skills exist across the board. - -- Knowledge of coding and scripting -- In-depth knowledge of container and container orchestration -- Knowledge of logging and configuration management -- Understanding of system administration -- In-depth knowledge of version control systems -- Knowledge of continuous integration and continuous deployment (CI/CD) -- Collaboration skills - -### Knowledge of coding and scripting - -To build a DevOps career, you should know at least one programming language and be proficient in scripting to further automate tasks and processes that would be tedious and slow. You should also be familiar with software development principles. - -### In-depth knowledge of container and container orchestration - -With micro-services popularity, applications can be shipped in containers and deployed to the cloud. It is possible with the help of tools like [Docker](https://roadmap.sh/docker) and container orchestration tools like [Kubernetes](https://roadmap.sh/kubernetes). A DevOps cloud engineer must have extensive knowledge of these tools and how to use them. - -![Docker and kubernetes](https://assets.roadmap.sh/guest/docker-and-kubernetes-kzopj.jpg) - -### Knowledge of logging and configuration management tools - -Monitoring is one of the core DevOps processes. In the DevOps career path, you are expected to have a knowledge of monitoring and logging tools. A popular one used is [Grafana](https://grafana.com/). You should be comfortable working with configuration management tools, automation frameworks, and Linux environments. - -### Understanding of system administration - -A basic understanding of provisioning and managing servers, security monitoring, and networks is required in the DevOps career path. You will monitor the servers for security vulnerabilities and apply patches when necessary. - -### In-depth knowledge of version control systems and source code management - -Version control is one of the DevOps tools, and required technical skills that a DevOps engineer should have. One of the common Version Control Systems(VCS) out there is Git. - -### Knowledge of continuous integration and continuous deployment (CI/CD) - -A DevOps professional is required to have a deep understanding of CI/CD. CI/CD involves the design and implementation of software delivery pipelines. It enables faster software release cycles. Some key DevOps tools include [Jenkins](https://www.jenkins.io/), [Azure DevOps](https://azure.microsoft.com/de-de/products/devops), [CircleCI](https://circleci.com/), [BitBucket Pipelines](https://bitbucket.org/), [GitHub Actions](https://github.com/features/actions), etc. - -### Communication and Collaboration skills - -As a DevOps professional, be prepared to work closely with cross-functional development teams. You are expected to have good communication and collaboration skills to be an effective team member. You should be able to clearly communicate your ideas to other developers, end-users and stakeholders. - -## How can I start my DevOps career? - -The next question you might be asking is how do I start my career in [DevOps](https://roadmap.sh/devops). -You can begin your DevOps career by obtaining a bachelor's degree in computer science degree from a college. - -You can also obtain DevOps certification from certified DevOps trainers. One of the popular DevOps certifications is the AWS Certified DevOps Engineer. - -roadmap.sh offers step-by-step guidance on [how to become a DevOps engineer](https://roadmap.sh/devops/how-to-become-devops-engineer), and by signing up, you will be able to: - -- Keep track of your progress and also share it on your roadmap.sh profile. -- Collaborate on other official roadmaps. -- Draw your [roadmap](https://draw.roadmap.sh/), either as an individual learner or for [Dev](https://roadmap.sh/teams) [team](https://roadmap.sh/teams). -- Generate new roadmaps with [AI](https://roadmap.sh/ai). diff --git a/src/data/guides/devops-engineer.md b/src/data/guides/devops-engineer.md deleted file mode 100644 index e58dd2fad..000000000 --- a/src/data/guides/devops-engineer.md +++ /dev/null @@ -1,309 +0,0 @@ ---- -title: 'What is a DevOps Engineer? Responsibilities & Roles in @currentYear@' -description: 'Explore the responsibilities and roles of a DevOps Engineer in @currentYear@. Gain insights into the evolving field of DevOps and what it takes to succeed.' -authorId: ekene -excludedBySlug: '/devops/devops-engineer' -seo: - title: 'What is a DevOps Engineer? Responsibilities & Roles in @currentYear@' - description: 'Explore the responsibilities and roles of a DevOps Engineer in @currentYear@. Gain insights into the evolving field of DevOps and what it takes to succeed.' - ogImageUrl: 'https://assets.roadmap.sh/guest/what-is-devops-engineer-jort4.jpg' -isNew: false -type: 'textual' -date: 2024-08-15 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![What is a DevOps engineer and what are their responsibilities?](https://assets.roadmap.sh/guest/what-is-devops-engineer-jort4.jpg) - -Are you a developer monitoring recent changes in the ecosystem, looking to change careers or pick up new skills in 2025? If your choice is [DevOps](https://roadmap.sh/devops), you might be wondering what it entails, what it will take to become one in 2025, and how it is affected by the recent changes in the tech ecosystem. - -In recent years, the technology ecosystem has experienced a constant shift in the way hiring managers reach out, companies hire, and the roles and responsibilities described in job postings. Particularly, 2023 proved to be a challenging year as layoffs in the technology sector grew significantly, with more than [262,000 employees laid off across 1,180 firms](https://www.statista.com/statistics/199999/worldwide-tech-layoffs-covid-19/). - -Despite this change, DevOps, a field within the same ecosystem, has experienced continuous growth. In fact, the DevOps market size is expected to grow to [25.5 billion USD by 2028](https://www.marketsandmarkets.com/Market-Reports/devops-market-824.html#:~:text=The%20global%20DevOps%20market%20size,USD%2010.4%20billion%20in%202023.). This indicates that the roles and responsibilities of a DevOps engineer in the modern technology environment will evolve alongside this increasing demand. - -In this guide, we'll discuss the roles and responsibilities of a DevOps engineer, the importance of DevOps in teams, common roles within a DevOps team, and best practices for DevOps teams. Finally, the guide will offer roadmaps for your DevOps journey. - -A DevOps engineer's roles and responsibilities include: - -- Following best practices and utilizing Infrastructure as Code (IaC) tools to design, implement, and manage infrastructure. - -- Implementing and managing Continuous Integration and Continuous Delivery (CI/CD) pipelines to automate software and code releases. - -- Facilitating and promoting interpersonal skills between the development teams, operations teams, and other stakeholders to establish productivity and continuous improvement. - -- Implementing monitoring, logging, alerts, and dashboards to track the performance and health of applications and infrastructure. - -- Writing and maintaining scripts to automate tasks and DevOps processes. - -- Providing support and troubleshooting issues related to applications, systems, and infrastructure. - -- Efficiently manage and monitor cloud resources by implementing autoscaling and other cloud management techniques to maintain optimal performance. - -Let's look at the DevOps ecosystem in detail. - -## What is a DevOps engineer? - -A DevOps engineer is a professional whose primary purpose is to work at the intersection of software development and IT operations. Their main goal is to continuously deliver high-quality software, improve collaboration between teams, and shorten the Software Development Life Cycle (SDLC). - -DevOps engineers are responsible for maintaining the culture and practices that bridge the gap between the software development process (Dev) and IT operations (Ops). They promote the use of tools and other automation mechanisms to shorten the software delivery processes while maintaining high quality. - -DevOps engineers use various tools to automate and enhance their workflow, such as: - -- Docker -- Kubernetes -- Jenkins -- Version control systems -- Grafana -- Ansible -- Prometheus - -### Docker - -Docker is a containerization platform that allows developers to package applications and their dependencies into a lightweight and portable container that can run consistently in any environment. Docker streamlines the development process by making it easy to manage and scale applications - -![Docker](https://assets.roadmap.sh/guest/docker-logo-cvtzr.jpg) - -You can learn more details by exploring the [Docker roadmap](https://roadmap.sh/docker). - -### Kubernetes - -Kubernetes is a container orchestration platform for automating, deploying, and scaling containerized applications. It offers numerous benefits, including cluster management, high availability, load balancing, and resource management. - -![Kubernetes](https://assets.roadmap.sh/guest/kubernetes-logo-zipbs.jpg) - -You can learn more details by exploring the [Kubernetes roadmap](https://roadmap.sh/kubernetes). - -### Jenkins - -Jenkins is an open-source automation tool for automating CI/CD processes. It gives developers the flexibility to build, test, and deploy applications. Jenkins' ecosystem and seamless integration with various tools make it ideal for automating DevOps workflow. - -![Jenkins](https://assets.roadmap.sh/guest/jenkins-logo-pqnqc.jpg) - -### Version control systems - -Version control systems allow developers to track, revise, manage branches, and merge codes seamlessly. Examples include Git, Mercurial, and Subversion, with popular platforms like GitHub and Bitbucket providing integration support. - -![Version control systems](https://assets.roadmap.sh/guest/git-logo-hrzut.jpg) - -### Grafana - -Grafana is an analytic and monitoring platform for visualizing metrics, logs, and other critical data. It helps DevOps engineers monitor the real-time health and performance of applications and systems. - -![Grafana](https://assets.roadmap.sh/guest/grafana-logo-zyx92.jpg) - -### Ansible - -Ansible is an automation tool for configuring application management, deployment, and other automation tasks. It uses a YAML syntax to define automation workflow, reducing the time required for repetitive tasks. - -![Ansible](https://assets.roadmap.sh/guest/ansible-logo-efs80.jpg) - -### Prometheus - -Prometheus is a monitoring and alerting tool that collects metrics from applications and infrastructure. It stores these metrics in a time-series database, allowing for querying and visualization to detect potential issues. - -![Prometheus](https://assets.roadmap.sh/guest/pometheus-logo-v4pp4.jpg) - -## What are the responsibilities of a DevOps engineer? - -DevOps engineers wear multiple hats to perform their day-to-day tasks. They possess technical skills in the software development lifecycle and operations skills for maintenance, and they are responsible for bridging the gap and fostering collaboration between development and other stakeholders. - -DevOps engineer responsibilities include: - -- Infrastructure management -- Setting up and maintaining CI/CD pipelines -- Working closely with others and keeping communication flowing -- Keeping an eye on things by monitoring and logging -- Automating toil -- Handling deployments and managing cloud services -- Optimizing performance -- Software development and scripting -- Managing resources efficiently -- Supporting developers and troubleshooting issues -- Ensuring security across the infrastructure - -![DevOps engineer job description](https://assets.roadmap.sh/guest/devops-engineer-description-tro4n.jpeg) - -Now, let's look at these points in detail. - -### Infrastructure management - -A key responsibility of a DevOps engineer is to ensure the design, implementation, and management of a robust and scalable infrastructure. They use Infrastructure as Code (IaC) tools like Terraform or Ansible to automate infrastructure provisioning and configuration management. - -### CI/CD pipelines - -In the SDLC, new features, patches, and fixes are frequently deployed to meet customers' needs. To facilitate this, a DevOps engineer must set up a CI/CD pipeline to automate the testing environment and software releases, ensuring seamless integration and a smooth deployment process. - -![ci/cd](https://assets.roadmap.sh/guest/cicd-y1h56.jpeg) - -### Collaboration and communication - -DevOps engineer skills are essential for fostering an efficient and collaborative work environment. DevOps engineers champion clear communication (both oral and written) between project stakeholders. They believe in active communication and collaboration between the development team and other project stakeholders to ensure productivity, shared responsibilities, and continuous improvement. - -![collaboration & communication](https://assets.roadmap.sh/guest/collaboration-f4lfb.jpeg) - -### Automation - -One of the essential DevOps principles is automating manual and repetitive tasks. A DevOps engineer uses DevOps automation tools like Puppet or SaltStack to improve efficiency and reduce human error during deployment processes, scaling, and other operational tasks. - -![automation](https://assets.roadmap.sh/guest/automation-kani0.jpeg) - -### Deployment and cloud management - -DevOps engineers use containers and orchestration platforms like Docker and Kubernetes to facilitate efficient deployment, scaling, and management, whether in on-premise environments or cloud services like [Amazon Web Services (AWS),](https://roadmap.sh/aws) Microsoft Azure, or Google Cloud Platform. - -![Clouds deployment — AWS vs Azure vs GCP](https://assets.roadmap.sh/guest/cloud-management-qbcb0.jpeg) - -### Performance optimization - -A key responsibility of a DevOps engineer is to set up mechanisms to constantly audit systems, analyze performance, and implement load and stress testing. This guarantees that applications can handle high demand and spikes in traffic. - -### Software development and scripting - -While the DevOps ecosystem offers both paid and open-source tools to help streamline responsibilities, there are times when custom-made tools or extensions of existing tools are required. A good understanding of software development processes and scripting languages like [Python](https://roadmap.sh/python), [Go](https://roadmap.sh/golang), or [JavaScript (Node.js)](https://roadmap.sh/nodejs) helps a DevOps engineer build solutions for such specific business edge cases. - -### Resource management - -In a DevOps engineer role, engineers ensure that the resources allocated to a system can perform effectively during both slow and peak periods. This involves implementing autoscaling, monitoring resource usage, considering future capacity needs, and utilizing other resource configuration management tools. - -### Developer support and troubleshooting - -An integral part of DevOps methodology is ensuring that development and operational teams are supported by troubleshooting issues related to applications, infrastructures, and other systems. Additionally, DevOps professionals create documentation, guides, and other helpful resources to enhance the knowledge base. This guarantees that both experienced team members and new joiners can leverage these resources for debugging, troubleshooting, or research purposes. - -### Security - -A growing part of the DevOps ecosystem is Development, Security, and Operations (DevSecOps). This practice involves automating security and best practices into every phase of the SDLC to ensure compliance with industry standards, effective resource identity and access management, and more. A good understanding of DevSecOps is an essential skill for experienced DevOps engineers. - -## What is the importance of DevOps in an organization? - -DevOps is gaining massive adoption because organizations are shifting away from traditional software development and resource provisioning approaches that are slow, prone to human error, and siloed. They want to deliver fast, reliable, and scalable applications, which the traditional approach lacks. - -Some of the ways DevOps can benefit an organization include: - -- Improving collaboration and shared responsibilities among project stakeholders. -- Enhancing software quality with automated testing and continuous feedback loops. -- Speeding up software delivery by promoting communication and collaboration between cross-functional teams. -- Supporting career development through a DevOps culture of experimentation and learning new DevOps skills. -- Providing a better customer experience because applications are thoroughly tested before going live. -- Reducing risk associated with software development and deployment using CI/CD principles. -- Encouraging knowledge sharing and flexibility to experiment, helping teams learn a wide range of skills and technologies. - -Organizations practicing or willing to adopt DevOps must be prepared for a cultural shift, continuous improvement, and investment in automation tools. Although it may be a lot at times, the benefits are worth the investment. - -## Common roles within the DevOps ecosystem - -The recent growth in the DevOps ecosystem has led to newer DevOps roles. While most jobs categorize these roles under the umbrella term "DevOps Engineer," which may work for small organizations, it becomes an issue in medium to larger organizations with diverse project goals and other factors. While these roles share similarities and sometimes overlap in responsibilities, it is important to understand the different aspects they tackle. The diagram below shows the crucial roles in the DevOps lifecycle. - -![Roles in DevOps](https://assets.roadmap.sh/guest/6-devops-roles-xc82k.jpeg) - -### Site Reliability engineers (SRE) - -SRE focuses on system reliability, scalability, and performance. Their skills involve combining software engineering and systems administration. - -**Key Responsibilities** - -- Implements monitoring and alerting systems. -- Ensures the system is reliable, performant, and scalable. -- Analyze incidents to detect them and implement preventive measures. - -**Required skills** - -- Proficiency in using automation and infrastructure as code tools. -- Problem-solving and troubleshooting skills. -- Experience with alerting and monitoring tools like Prometheus and Grafana. - -### Cloud engineers - -Cloud engineers are responsible for designing and implementing cloud-native solutions. They also manage cloud technologies and infrastructure like AWS, Azure, or Google Cloud to guarantee efficient resource usage and cost-effectiveness. - -**Key Responsibilities** - -- Design and manage cloud infrastructure. -- Monitor and optimize cloud resources and associated costs. -- Ensure compliance and security of cloud resources. - -**Required skills** - -- Expertise in using popular cloud providers like AWS, Azure, and GCP. -- Proficiency in using automation tools like Terraform and CloudFormation. -- Knowledge of cloud security and compliance - -### Automation engineers - -Automation engineers are responsible for identifying tasks that can be automated across development and operations. They develop scripts and DevOps tools to automate repetitive tasks and integrate them into CI/CD pipelines. - -**Key Responsibilities** - -- Review, design, and implement automation strategies across the software development lifecycle. -- Ensures consistency and reliability of automated processes. -- Stay up-to-date with the latest automation tools and technologies. - -**Required skills** - -- Proficiency in scripting and programming languages. -- Expertise in automation tools and frameworks like Jenkins and GitHub Action. - -### Build engineers - -Build engineers are responsible for managing build systems and tools, troubleshooting build failures, optimizing build performance, and integrating builds into the CI/CD pipelines. - -**Key Responsibilities** - -- Manage and maintain build systems and tools. -- Ensures builds are consistent and replicable across environments. -- Automate the build process. - -**Required skills** - -- Knowledge of CI/CD tools and version control systems. -- Proficiency in build tools. -- Strong problem-solving and debugging skills. - -### Release engineers - -Release engineers are responsible for planning, scheduling, and coordinating the software release process for testing, sandbox, or production environments. - -**Key Responsibilities** - -- Plan and manage the release of new software, software updates, and new features. -- Maintain release schedules and project release timelines. - -**Required skills** - -- Knowledge of CI/CD tools and version control systems. -- Project management and organization skills. -- Strong communication and coordination skills - -### Security engineers - -Security engineers are responsible for integrating security and best practices into the DevOps pipeline. Additionally, they collaborate with development and operations teams to conduct security assessments, vulnerability scans, and compliance checks. - -**Key Responsibilities** - -- Implement and manage security tools and technologies. -- Integrate DevSecOps into the software development lifecycle. -- Document security best practices and ensure compliance with policies and regulations. - -**Required skills** - -- Knowledge of security best practices and frameworks -- Familiarity with identity and access management systems - -## Best practices for DevOps teams - -To achieve success as a team practicing or intending to adopt DevOps practices, you need a combination of the right tools, best practices, and a cultural shift. Here are some tips and best DevOps practices for teams: - -- Encourage Continuous Integration (CI) and Continuous Deployment (CD) by having software developers commit code frequently, automating builds, and providing timely feedback between teams. -- Embrace IaC to maintain consistency across testing, production, and other environments. -- Implement logging, alerts, and monitoring across systems. -- Ensure unit tests, integration tests, and end-to-end tests to validate the quality of software. -- Promote soft skills, feedback loops, post-mortem audits, retrospectives, and a continuous learning culture. -- Set clear metrics and Key Performance Indicators (KPIs) to easily identify areas needing improvement. -- Ensure effective collaboration and shared responsibilities between development, operations, QAs, and other teams. - -In summary, DevOps is growing and will continue to expand, bringing about even more DevOps roles and responsibilities for engineers in the ecosystem. The roadmap.sh’s [DevOps roadmap](https://roadmap.sh/devops) is a valuable resource that can help you stay updated on these changes. Additionally, you can track your learning path and connect with a vibrant community by [signing up](https://roadmap.sh/signup) on the roadmap.sh platform. diff --git a/src/data/guides/devops-job-description.md b/src/data/guides/devops-job-description.md deleted file mode 100644 index 13c322896..000000000 --- a/src/data/guides/devops-job-description.md +++ /dev/null @@ -1,235 +0,0 @@ ---- -title: 'DevOps Engineer Job Description [@currentYear@ Template]' -description: 'Create the perfect DevOps Engineer job description with our @currentYear@ template, tailored to attract top talent in today''s tech landscape.' -authorId: william -excludedBySlug: '/devops/job-description' -seo: - title: 'DevOps Engineer Job Description [@currentYear@ Template]' - description: 'Create the perfect DevOps Engineer job description with our @currentYear@ template, tailored to attract top talent in today''s tech landscape.' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-engineer-job-description-0xjml.jpg' -relatedTitle: 'Other Guides' -relatedGuidesId: 'devops' -isNew: false -type: 'textual' -date: '2025-01-17' -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![DevOps engineer job description template](https://assets.roadmap.sh/guest/devops-engineer-job-description-0xjml.jpg) - -As businesses adopt agile practices to stay competitive, the demand for skilled DevOps professionals is on the rise. Hiring top talent starts with understanding their role and crafting a clear, compelling job description. - -To help you create the ideal profile, this guide draws insights from top job boards like Indeed, LinkedIn, and Glassdoor. It covers the key responsibilities, essential skills, and qualifications of a [DevOps engineer](https://roadmap.sh/devops). Use this template as your go-to resource for attracting the best candidates in the field. - -## DevOps engineer job description template - -A DevOps engineer ensures seamless collaboration between software development and IT operations to improve software delivery speed and system reliability. Here is a DevOps engineer job description template that highlights the essential skills and qualifications that hiring managers look for in potential candidates. - -**Job Title:** DevOps Engineer - -**Company:** [Add your company name] - -**Location:** [Specify your location] - -**Job Type:** Full-time or part-time - -**About Us:** [Provide company name and a quick summary of their achievements, history, and goals] - -**Job description** - -**[Company Name]** is looking for an experienced DevOps engineer with strong technical expertise in CI/CD pipelines, infrastructure automation, and cloud platforms, along with excellent collaboration and communication skills. The candidate should have hands-on experience with configuration management tools, a solid understanding of DevOps practices, and a working knowledge of internal backend systems. The ideal candidate will have the ability to coordinate and bridge gaps between the software developer and the operation team, ensuring a smooth workflow. - -**Responsibilities** - -DevOps engineer's responsibilities include: - -- Designing and maintaining continuous integration/continuous deployment pipelines to automate code testing and deployment -- Tracking software performance, fixing errors, troubleshooting systems, implementing preventive measures for smooth workflows, and building automated processes -- Optimizing cloud resources and implementing cost-effective solutions -- Implementing and managing infrastructure using code rather than manual processes. Uses Terraform or CloudFormation for Infrastructure as Code (IaC) management -- Collaborating between teams helps in resolving issues quickly and deploying new features smoothly -- Monitoring and creating new processes based on performance analysis -- Better management of the software development process and implementation of configuration management tools -- Automating repetitive tasks to improve team efficiency -- Implementing security best practices, including automated compliance checks and secure code deployment - -**Requirements** - -The candidate must fulfill the following requirements for the DevOps engineering job profile: - -- Hands-on experience with CI/CD tools -- Good experience in infrastructure as code tools -- Familiarity with monitoring and logging tools -- Proficiency in Docker for packaging applications and Kubernetes for managing containers -- Strong problem-solving and troubleshooting skills -- Excellent communication and collaboration skills to ensure proper teamwork -- Proficiency in scripting languages like Python, PowerShell, etc. -- A Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent industry experience - -For an entry-level DevOps engineer role, recruiters might look for: - -- Basic knowledge of DevOps tools, i.e., Git, Jenkins, and Docker -- Familiarity with at least one programming language, such as Python or Go -- Understanding of basic networking concepts -- Willingness to embrace continuous learning and adoption of new tools - -**Nice to have** - -- Certification in cloud platforms like AWS certified DevOps engineer -- Good knowledge of agile methodologies and practices - -**What we offer** - -Highlight what your company offers, such as competitive salary, health benefits, professional development opportunities, flexible work arrangements, or other perks. - -**How to apply** - -If you are interested in the DevOps engineer job profile, send your resume and cover letter to your selected company [insert email address] or apply through [insert application portal link]. - -## Key DevOps engineer skills essential for job profile - -When recruiting a DevOps engineer, prioritize candidates with strong technical expertise, adaptability, and collaboration skills. DevOps requires bridging development and operations, so focus on individuals who excel in both technical problem-solving and teamwork. - -![Key DevOps engineer skills](https://assets.roadmap.sh/guest/key-devops-engineer-skills-essential-for-job-profile-qhr33.png) - -Let's explore the essential skills that make a strong DevOps engineer: - -### Technical skills - -A DevOps job description must showcase a strong technical background and knowledge of critical concepts. Future engineers must know how to maintain tools, perform root cause analysis, develop project management capabilities, etc. A DevOps engineer must possess the following technical skills: - -- **Proficiency with CI/CD tools:** Familiarity with CI/CD tools, such as Jenkins, GitLab CI/CD, or Bamboo, to automate build and deployment processes -- **Coding and scripting skills:** Knowledge of programming languages like Python, Ruby, or Java, and scripting languages like Bash -- **Cloud platform expertise:** Experience with AWS or Google Cloud platform for managing scalable infrastructure -- **Containerization and orchestration:** Understanding of Docker and Kubernetes to deploy and manage containerized applications -- **Version control systems:** Expertise in Git for code repository management and facilitating team collaboration - -### Cross-functional skills - -Cross-functional skills are essential for DevOps engineers to enable seamless collaboration across teams and departments. These skills help bridge the gap between technical and non-technical stakeholders for successful project outcomes, such as: - -- **Problem-solving:** DevOps engineers must have the ability to quickly identify and resolve system bottlenecks or failures -- **Communication:** Strong communication skills to explain technical issues and collaborate effectively with multiple teams -- **Collaboration:** DevOps engineers must have a team-oriented mindset to bridge gaps between development, operations, and business stakeholders - -Equipped with an understanding of the essential skills, the next step is evaluating candidates' familiarity with popular DevOps tools. Here's a quick guide to the tools recruiters should expect to see in job descriptions and interviews. - -## Common DevOps tools and technologies - -Candidates with experience in the following tools are often preferred, as they are essential for automating workflows and ensuring system reliability. Let's explore some of the most popular DevOps tools that are commonly included in job descriptions. - -![Common DevOps tools and technologies](https://assets.roadmap.sh/guest/common-devops-tools-and-technologies-dwrxx.png) - -### Jenkins - -Jenkins is a popular open-source automation server used for continuous integration and continuous delivery (CI/CD). It streamlines software testing and deployment, reducing manual effort and accelerating development cycles. Jenkins offers several features, including: - -- **Extensive plugin library:** Supports tools like Docker, Kubernetes, and Git for seamless integration -- **Real-time feedback:** Alerts teams to failed builds, enabling early issue resolution -- **Customizable pipelines:** Offers flexibility through domain-specific language (DSL) or GUI-based pipelines - -### Docker - -Docker is a containerization platform that packages applications and their dependencies into lightweight containers. These containers work seamlessly across different phases, from software development to production. Some of the key features of Docker are: - -- **Isolated environment:** Maintains reliable application performance across different underlying systems -- **Faster deployment:** Enables quicker application deployment compared to traditional virtual machines -- **Support for microservices architecture:** Facilitates modular development, making it easier to build, deploy, and scale microservices-based applications - -Discover additional use cases and strategies for [Docker](https://roadmap.sh/docker) in modern application development. - -### Kubernetes - -Kubernetes is an open-source orchestration platform for automating the deployment, scaling, and management of containerized applications. It works smoothly with Docker and other container runtimes to guarantee application reliability and scalability. Some standout features make Kubernetes a powerful choice, including: - -- **Automated scaling:** Dynamically allocates resources to accommodate fluctuating workloads -- **Self-healing capabilities:** Automatically restarts failed containers or replaces unresponsive nodes to maintain application health -- **Service discovery and load balancing:** Efficiently distributes traffic across services, improving resource utilization and application performance - -Learn how [Kubernetes](https://roadmap.sh/kubernetes) supports advanced DevOps workflows and container orchestration. - -### Terraform - -Terraform by HashiCorp is a tool for defining and provisioning infrastructure using declarative configuration files. This approach enables teams to automate the setup and management of cloud resources, maintaining consistency and reducing errors. Key features of Terraform include: - -- **Multi-cloud support:** Supports multiple cloud providers, including AWS, Google Cloud, etc. -- **Version control:** Tracks infrastructure changes, enabling safe rollbacks and thorough audits -- **Reusable modules:** Simplifies infrastructure management with reusable and shareable code - -Explore how [Terraform](https://roadmap.sh/terraform) empowers teams to manage modern infrastructure efficiently. - -### Git - -Git is a distributed version control system that allows developers to track code changes, collaborate on projects, and maintain a complete history of all modifications. Some of the key features of Git are: - -- **Branching and merging:** Enables developers to work on different tasks simultaneously -- **Support for distributed workflows:** Enables offline work and seamless collaboration, providing flexibility for teams -- **Platform integration:** Integrates with platforms like GitHub, GitLab, and Bitbucket to streamline project management - -### Prometheus and Grafana - -Prometheus and Grafana are often used together to monitor and visualize application performance. - -- **Prometheus:** A powerful open-source monitoring system designed for metrics collection and alerting. Features a robust query language (PromQL) for analyzing time-series data -- **Grafana:** A visualization tool that creates interactive dashboards using data from Prometheus and other sources. Enables teams to monitor key metrics in real time - -Prometheus collects and stores metrics, while Grafana visualizes these metrics in customizable dashboards. This combination empowers teams to: - -- Track application performance in real time -- Diagnose and resolve system issues efficiently -- Set up alerts to ensure minimal downtime and maintain service reliability - -Equipped with knowledge about essential tools, it's time to explore career growth opportunities and roles within your organization that align with a DevOps engineer's expertise. - -## Growth opportunities and the importance of continuous learning - -By continuously building skills and staying ahead of industry trends, DevOps engineers can advance their careers and take on more strategic roles within an organization. - -For hiring managers, understanding how these roles contribute to business outcomes is key to aligning talent acquisition strategies with organizational goals. By fostering career growth, organizations not only enhance employee satisfaction but also build a workforce capable of driving innovation and operational efficiency. - -A candidate can choose from the different career options available in the future, such as: - -### DevOps architect - -A [DevOps architect](https://roadmap.sh/devops/devops-engineer) is responsible for designing enterprise-level DevOps frameworks and strategies. This role involves creating scalable frameworks, integrating tools and processes, and aligning DevOps practices with business objectives. - -This role requires a deep understanding of cloud technologies, automation tools, and CI/CD pipelines to ensure seamless software delivery. DevOps architects also focus on enhancing collaboration among cross-functional teams and aligning technical initiatives with organizational goals. Their expertise helps businesses achieve faster deployments, improved quality, and greater operational efficiency. - -### Site reliability engineer (SRE) - -Site reliability engineers focus on maintaining system reliability and performance by leveraging automation and proactive monitoring. They develop robust recovery plans and address potential bottlenecks before they impact users. - -SREs minimize downtime and enhance user experience, ensuring high availability of critical systems and boosting customer satisfaction. Their expertise directly reduces operational risks and strengthens business continuity. - -### Cloud engineer - -Cloud engineers specialize in managing cloud infrastructure and optimizing cloud-based solutions. They oversee multi-cloud or hybrid cloud environments while implementing security measures to protect resources. - -Cloud engineers are well-versed in cloud providers like AWS or Google Cloud and work on automating resource provisioning, monitoring, and scaling to accommodate evolving business needs. They also play a crucial role in implementing cloud security measures and ensuring compliance with industry standards, enabling organizations to leverage the full potential of cloud technologies. - -### Consultant or advisor - -Consultants or advisors help organizations adopt DevOps best practices, select the right tools, and train teams to foster continuous improvement. - -They play a critical role in driving organizational transformation by aligning DevOps initiatives with business objectives and empowering software engineering and operation teams with the skills and strategies needed to achieve long-term success in a competitive landscape. - -IT dynamics are constantly changing, and staying relevant in the field of DevOps requires a commitment to continuous learning. Organizations that invest in the growth of their DevOps teams reap significant rewards: - -- **Retention of top talent:** Employees are more likely to stay with companies that support their professional growth. Offering opportunities for skill development, DevOps training, certifications, and career advancement fosters loyalty and reduces turnover -- **Enhanced operational efficiency:** A well-trained DevOps team can implement cutting-edge tools and agile principles, improving workflow efficiency and reducing downtime. This directly translates to better product delivery and customer experience -- **Attracting skilled candidates:** Highlighting growth opportunities in job descriptions makes the organization more appealing to skilled candidates. Professionals in this field actively seek DevOps engineer roles where they can grow and contribute better -- **Fostering innovation:** This practice helps employees to experiment with new technologies and methodologies, driving innovation. Organizations encouraging this culture remain competitive and adaptive in a rapidly changing market - -## What next? - -To take the next step: - -- Use the above **DevOps engineer job description template** to simplify your hiring process -- Explore our [**DevOps roadmap**](https://roadmap.sh/devops) for deeper insights into career paths and the skills that matter most -- Join the [Discord community](https://roadmap.sh/discord) to stay informed about the latest updates and meaningful discussions \ No newline at end of file diff --git a/src/data/guides/devops-lifecycle.md b/src/data/guides/devops-lifecycle.md deleted file mode 100644 index 66a8b2f2b..000000000 --- a/src/data/guides/devops-lifecycle.md +++ /dev/null @@ -1,261 +0,0 @@ ---- -title: 'What Are the 7 Key Phases of the DevOps Lifecycle?' -description: 'Master the DevOps lifecycle by exploring its 7 phases, designed to enhance collaboration, streamline processes, and deliver software with agility.' -authorId: william -excludedBySlug: '/devops/lifecycle' -seo: - title: 'What Are the 7 Key Phases of the DevOps Lifecycle?' - description: 'Master the DevOps lifecycle by exploring its 7 phases, designed to enhance collaboration, streamline processes, and deliver software with agility.' - ogImageUrl: 'https://assets.roadmap.sh/guest/key-phases-of-devops-lifecycle-788fa.jpg' -relatedGuidesTitle: 'Other Guides' -relatedGuidesId: devops -isNew: false -type: 'textual' -date: 2024-11-01 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Learn the key phases of the DevOps lifecycle.](https://assets.roadmap.sh/guest/key-phases-of-devops-lifecycle-788fa.jpg) - -Whether you’re an experienced DevOps engineer or trying to expand your expertise, you’ll likely adopt (or are already using) parts of the **7 key phases of the DevOps lifecycle** as a core **process** for developing, testing, and deploying software projects. - -But what are these phases, and do they really need to be followed in a specific order to truly matter? - -As a [DevOps engineer](https://roadmap.sh/devops), your primary role is to help the development and operations teams operate better. You’ll do this by collaborating closely with software engineers, quality assurance teams, and other stakeholders to set up **processes**, implement **[tools](https://roadmap.sh/devops/tools)**, and create standards to achieve the overall goal of the project. - -In this guide, you’ll learn about these phases, how they're implemented, and the sequence in which they are applied in software development. - -**TL;DR:** 7 key phases of the DevOps lifecycle are: - -- Continuous development -- Continuous integration (CI) -- Continuous testing -- Continuous deployment (CD) -- Continuous monitoring -- Continuous feedback -- Continuous operations - -Let’s look at the DevOps lifecycle in detail. - -## What is the DevOps lifecycle? - -DevOps lifecycle is a set of stages that software development (Dev) and IT operations (Ops) teams use to deliver software applications in an efficient and reliable manner. It is a continuous and iterative process that facilitates integration and collaboration between these teams. - -In traditional software development, developers don’t just build and deploy applications. They must also accommodate changes, fix bugs, consider feature requests, and handle various administrative tasks. The same approach to continuous improvement applies in DevOps, which has led to industries adopting DevOps to factor in the lifecycle processes into their operations. - -The primary goal of the DevOps lifecycle is to streamline your development and delivery process and ensure applications are reliable and efficiently deployed. - -It follows a range of continuous development, integration, testing, monitoring, and feedback gathering, with each section using sets of best practices and tools to ensure the overall project goal. - -![DevOps Lifecycle](https://assets.roadmap.sh/guest/7-key-phases-of-the-devops-lifecycle-4zoj6.png) - -## 7 key phases of the DevOps lifecycle - -The 7 key phases of the DevOps lifecycle, also known as the 7 C’s of DevOps, are sets of interconnected stages that work together in a continuous loop to help you develop, test, and deploy applications quickly. Below are the key phases of the DevOps lifecycle: - -### 1. Continuous development - -This phase is about planning and coding the software application. Developers plan the software and break the entire development process into smaller cycles that add value to the overall software development goal. - -![Continuous Development](https://assets.roadmap.sh/guest/continuous-development-xbxj3.png) - -By following this process, DevOps teams can easily map out the **software development lifecycle (SLDC)** to other stakeholders regarding expectations, responsibilities, and timelines. Additionally, because the development teams, testers, and other stakeholders build software piece-by-piece, the development process is fast, large-scale risk is minimal, and the process can easily adapt to changing requirements and business needs. - -**Tools used for continuous development** - -1. **Planning:** DevOps teams use project management tools like Jira, Linear, and ClickUp to help teams plan, track, and release software. - -2. **Coding**: DevOps teams can use version control systems like Git, editors like Visual Studio Code, and pair programming tools like Tuple to effectively collaborate with other development teams when building software. - -### 2. Continuous integration (CI) - -After writing the code and storing it in a shared repository, DevOps teams can set up a CI pipeline on the repository so that when developers commit changes to the source code, they can do the following: - -- Detect changes to existing code and initiate actions like unit testing, integration testing, and the build process. -- Perform code quality analysis. -- Generate deployment artifacts. - -![Continuous Integration](https://assets.roadmap.sh/guest/continuous-integration-b4lef.png) - -This is particularly important because the development team will continue to push updates into the source code to build new features, fix bugs, perform code improvement, and refactoring. - -**Tools used** - -Jenkins, CircleCI, Travis CI, and GitHub Actions are some [automation tools](https://roadmap.sh/devops/automation-tools) DevOps teams use to build, test, and deploy code changes. - -### 3. Continuous testing - -Continuous testing involves automating tests on the developed code to ensure that changes are validated at each step of the development cycle, catch defects, and provide feedback without the need for human intervention. - -![Continuous testing](https://assets.roadmap.sh/guest/continuous-testing-d90gb.png) - -If an error or bug occurs, the code is returned to the previous phase (integration) for correction and possible fixes. Automated testing improves the overall workflow by saving time and resources. - -**Tools used** - -Selenium, JUnit, TestNG, and Cucumber are some automation testing tools that DevOps teams use to automate testing at scale. - -### 4. Continuous deployment (CD) - -This is the phase when the codes that have passed all tests are automatically deployed to the staging or production environment. Continuous deployment's overall goals are: - -- Reduce the time between development and deployment. -- Facilitate the deployment of finished code to production servers. -- Ensure consistency across development, testing, staging, and production environments. - -![Continuous Deployment](https://assets.roadmap.sh/guest/continuous-deployment-bprfv.png) - -**Tools used** - -1. **Configuration tools**: The DevOps team uses configuration management tools like Ansible, Puppet, Chef, and SaltStack to automate the provisioning, configuration, management, and continuous delivery of IT infrastructure. These tools help the DevOps team increase efficiency, maintain consistency across environments, and reduce errors. - -2. **Containerization and orchestration tools**: The DevOps team uses tools like [Docker](https://roadmap.sh/docker), Vagrant, and [Kubernetes](https://roadmap.sh/kubernetes) to build and test applications. These tools help applications respond to demand (scaling up and scaling down) and maintain consistency across environments. - -### 5. Continuous monitoring - -This is the phase where you keep an eye on the deployed application to monitor performance, security, and other helpful data. It involves the collection of metrics and other application usage-related data to detect issues such as system errors, server downtime, application errors, and security vulnerabilities. Additionally, it involves collaboration with the operation teams to monitor bugs and identify improper system behavior. - -![Continuous Monitoring](https://assets.roadmap.sh/guest/continuous-monitoring-cydj0.png) - -Continuous monitoring improves the productivity and reliability of the system while reducing IT support costs. Any issues detected during this phase can be promptly reported and addressed in the continuous development phase, creating a more efficient feedback loop. - -**Tools used** - -Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana), and Datadog are some tools DevOps teams use to continuously monitor the application and infrastructure to identify and resolve issues. - -### 6. Continuous feedback - -Continuous feedback is about gathering information from users and stakeholders to understand how the software performs in real-life scenarios. The feedback is then continuously analyzed and used to make informed decisions and improve the overall development process. - -![Feedback](https://assets.roadmap.sh/guest/continuous-feedback-eg1tr.png) - -**Tools used** - -DevOps teams use tools like Datadog and LogRocket to gather and gain insights into how users interact with their products. - -### 7. Continuous operations - -In the traditional software development process, developers might need to pull down the server when they want to update and maintain applications. This approach disrupts the development process, potentially increases organizational costs, and can lead to user service interruptions. - -![Continuous operations](https://assets.roadmap.sh/guest/continuous-operations-h2yrj.png) - -Continuous operations address these challenges, among others. It ensures the software remains available and operational with minimal downtime. This phase involves tasks such as: - -- Performing zero-downtime deployments. -- Automating backups and recovery. -- Using infrastructure management to provision and scale resources. -- Distributing traffic across multiple servers to maintain performance during updates or high-traffic periods. -- Implementing strategies like database replication and rolling updates to maintain data availability. - -**Tools used** - -Puppet, Terraform, and Chef are some tools DevOps teams use to automate resource provisioning and ensure system reliability. - -The DevOps lifecycle is a continuous process that involves development, integration, testing, deployment, monitoring, feedback, and operations. Beyond the improvement it brings, you’ll also notice that organizations are extending DevOps and further advancing its capability. - -Let’s explore some of these extensions and how they’re changing the development process. - -## Key DevOps extensions to watch in 2025 - -Below are some extensions that build on the core principles of DevOps, like [automation](https://roadmap.sh/devops/automation), collaboration, and continuous improvement: - -- DevSecOps -- GitOps -- DataOps -- FinOps -- MLOps -- AIOps - -![DevOps extension](https://assets.roadmap.sh/guest/devops-extensions-8fy7t.png) - -### DevSecOps - -DevSecOps stands for **Development**, **Security**, and **Operations**. It’s an extension of DevOps that continuously integrates security practices into every phase of the software development lifecycle rather than treating them as an afterthought. - -With the increase in cybersecurity threats and regulatory requirements, it has become more important to use DevSecOps to embed security into the pipeline so that organizations can deliver secure software faster. - -DevSecOps uses tools like HashiCorp Vault, Snyk, OWASP ZAP, and Aqua Security to: - -- Automate security testing. -- Perform continuous compliance. -- Enforce secure coding practices -- Perform vulnerability assessment. - -### GitOps - -GitOps stands for **Git Operations**. It’s an extension of DevOps that uses Git as a source of truth for managing infrastructure and application development. This means the DevOps teams can make changes to infrastructure through Git pull requests, which are then automatically applied via the CI/CD pipelines. - -By adopting GitOps, organizations can improve the reliability of their systems, enforce standards for the team, and accelerate software delivery. - -GitOps involves using tools like Jenkins X, Flux, and ArgoCD to automate the delivery and deployment of applications. - -### DataOps - -DataOps stands for **Data Operations**. It’s an extension of DevOps methodology designed to improve data pipeline communication, integration, and automation across the data and IT operations teams. DataOps aims to ensure that the data pipeline is fast, scalable, and reliable. - -DataOps uses tools like Apache NiFi, data build tool (dbt), and Prefect to: - -- Perform data versioning. -- Automate data testing. -- Automate the delivery of data pipelines. - -### FinOps - -FinOps stands for **Financial Operations**. It’s an extension of DevOps that enables organizations that use cloud services to efficiently manage their cloud costs and financial operations. The goal of FinOps is to optimize cloud-related costs by encouraging close collaboration between finance, operations, and engineering teams. - -![finOps](https://assets.roadmap.sh/guest/1tvyy8hg-f4fd8.png) - -FinOps also uses a lifecycle approach to optimize organization costs. It involves: - -1. **Inform**: This phase involves gaining visibility into cloud spending by tracking cloud costs, setting budgets, and leveraging discounts or other freebies offered by cloud providers. Basically, it provides the team insights into where the money is being spent. -2. **Optimize**: This phase is all about optimizing cloud costs. It involves sizing resources, identifying areas of inefficiency, and other cost-improvement tasks that will help make cost-effective decisions without compromising performance. -3. **Operate:** This phase is about monitoring cloud spending, enforcing policies, and making needed adjustments to ensure the budget is not exceeded. - -FinOps leverage tools like Azure Cost Management, AWS Cost Explorer, Cloudability, and CloudHealth to achieve the organization's cloud-related financial goals. - -### MLOps - -MLOps stands for **Machine Learning Operations**. It’s an extension of DevOps workflow that streamlines and automates the deployment, monitoring, and management of ML models in a production environment. It promotes collaboration between the data science and IT operations teams so that models can be versioned, continuously delivered, and retrained when needed. - -Tools used include TensorFlow Extended (TFX), Kubeflow, KitOps, and MLflow. - -### AIOps - -AIOps stands for **Artificial Intelligence for IT Operations**. It’s an extension of DevOps that promotes using artificial intelligence, machine learning, and data analytics to automate and improve IT operations processes. When AIOps is integrated into DevOps processes, the organization benefits from enhanced efficiency, faster issue resolution, and proactive system monitoring. - -Tools used include IBM Watson AIOps and Dynatrace. - -The extension of DevOps workflow is a response to modern software challenges, driven by the ongoing shift in the DevOps ecosystem and the need for specialized practices across different software engineering fields. - -## Essential DevOps lifecycle best practices - -An essential part of DevOps culture is the lifecycle phases. While the lifecycle phases streamline the operational process and help you build reliable software, there are still some gotchas that you need to consider when integrating this process into your SDLC. Below are some best practices you should consider: - -1. **Promote collaboration**: As a DevOps engineer, you need to encourage cross-functional collaboration and shared responsibilities among direct teams and other stakeholders. This will help you and your team avoid the traditional siloed approach, break communication barriers, and promote DevOps culture. - -2. **Show empathy and support**: Implementing DevOps lifecycle into your development process may take time and require some adjustment for you and your team members. You need to support the team with resources and any helpful training material to help facilitate the process. Most importantly, allow time for everyone to adapt to the new process. - -3. **Set metrics or milestones**: As the popular saying goes, **“You can’t manage what you can’t measure****.****”** You must set clear objectives and define performance metrics at the beginning or during the adoption of a new process. This will help you and your team know what success looks like. - -4. **Invest in tools**: At the heart of DevOps are the toolchains that automate toils and enable easy collaboration between development and operations teams. You should invest in DevOps tools that your team needs to automate their DevOps workflow. Below are some DevOps tools that can help you automate processes: - - **CI/CD tools**: Tools like Jenkins, GitLab CI/CD, CircleCI, Azure Pipeline, and GitHub Actions help automate the integration and deployment of code changes. - - **Infrastructure as Code (IaC) tools**: Tools like Terraform, Ansible, Pulumi, Chef, AWS CloudFormation, and Vagrant help automate the provisioning and management of infrastructure. - - **Containerization and orchestration tools**: Tools like Docker, Kubernetes, OpenShift, Docker Swarm, and Amazon ECS (Elastic Container Service) help manage and orchestrate containers at scale. - - **Monitoring and logging tools**: Tools like Prometheus, ELK Stack (Elasticsearch, Logstash, and Kibana), Datadog, Splunk, and Grafana help track system performance, logging, and alerting. - - **Configuration management tools**: Tools like Chef, Puppet, CFEngine, SaltStack, and Ansible help ensure that system configurations remain consistent across environments. - - **Security and compliance tools**: Tools like HashiCorp Vault, OWASP ZAP, Snyk, SonarQube, and Aqua Security help enforce security policies, scanning, and compliance checks. - - **Collaboration and communication tools**: Tools like Slack, Microsoft Teams, Trello, Jira, and Confluence help facilitate communication and collaboration between teams. - -5. **Continuous improvement**: Encourage your teams to share knowledge across teams, conduct service failure postmortem, and experiment with new ideas and potential solutions. - -## Key takeaways - -At the core of the DevOps lifecycle is continuity. By following these key phases in an iterative pattern, you’ll be able to take advantage of the lifecycle process to build applications that are maintainable, scalable, and reliable. - -Use the [DevOps roadmap](https://roadmap.sh/devops) to stay up to date with the latest developments and extensions in the DevOps ecosystem. Additionally, you can create a [custom roadmap](https://roadmap.sh/teams) for your team to plan, track, and document the team's skills and growth. diff --git a/src/data/guides/devops-principles.md b/src/data/guides/devops-principles.md deleted file mode 100644 index 24cd7f8ef..000000000 --- a/src/data/guides/devops-principles.md +++ /dev/null @@ -1,188 +0,0 @@ ---- -title: '11 Core DevOps Principles to Master' -description: 'Elevate your game by understanding this set of key DevOps principles and practices. Gain pro insights for a more efficient, collaborative workflow!' -authorId: fernando -excludedBySlug: '/devops/principles' -seo: - title: '11 Core DevOps Principles and to Master: Pro Advice' - description: 'Elevate your game by understanding this set of key DevOps principles and practices. Gain pro insights for a more efficient, collaborative workflow!' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-engineer-skills-tlace.jpg' -relatedGuidesTitle: 'Other Guides' -relatedGuidesId: devops -isNew: false -type: 'textual' -date: 2024-09-24 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![DevOps principles to master](https://assets.roadmap.sh/guest/devops-principles-pfswx.jpg) - -If you truly want to understand what makes DevOps so effective, it’s essential to know and master its core principles. - -DevOps is more than just a collaboration between development and operations teams; it's built on fundamental principles that simplify software delivery. - -In this guide, I’m going to dive deep into the core principles and practices that make the DevOps practice “tick.” If you’re a DevOps engineer or you want to become one, these are the DevOps principles you should master. - -I’ll explain the following principles in detail: - -1. Understanding the culture you want to join -2. CI/CD -3. Knowing how to use infrastructure as code tools. -4. Understanding containerization. -5. Monitoring & observability. -6. Security -7. Reducing the toil and technical debt. -8. Adopting GitOps. -9. Understanding that you’ll be learning & improving constantly. -10. Understanding basic programming concepts. -11. Embracing automation. - -## 1\. Understanding DevOps Culture - -[DevOps](https://roadmap.sh/devops) culture is the foundation for all DevOps principles. At its core, it's about fostering a collaborative environment where development and operations teams work together seamlessly. In traditional software development, developers focus on writing code while the operations team is tasked with deploying and maintaining it. This division often leads to misunderstandings and delays. - -Instead of operating in silos, these teams, when they follow the DevOps culture, end up sharing a common goal: delivering high-quality software efficiently. This cultural shift reduces the "us versus them" mentality that many organizations suffer, fostering cooperation instead of blame. - -DevOps culture encourages development and operations to collaborate throughout the software development lifecycle (SDLC). By aligning their goals and encouraging open communication, both teams can work together to improve the process of development, ultimately resulting in faster and more reliable software delivery. - -Key components of this culture include shared responsibility, transparency, and a commitment to continuous improvement. - -## 2\. Continuous Integration and Continuous Deployment (CI/CD) - -![Continuous Integration and Continuous Deployment](https://assets.roadmap.sh/guest/continous-development-vs-continuous-integration-l2fak.png) - -Continuous Integration (CI) and Continuous Deployment (CD) are central to DevOps principles. CI is the practice of frequently integrating code changes into a shared repository, ensuring that new code is [automatically tested](https://roadmap.sh/devops/test-automation) and validated. This practice helps catch bugs early, reducing the risk of introducing issues into the main codebase. CI allows devs and ops teams to work more efficiently, improving the overall quality of the software. - -Continuous Deployment, on the other hand, takes things a step further by automatically deploying code changes to production once they pass the CI tests. This ensures that new features and bug fixes are delivered to users as quickly as possible. Together, CI and CD form a pipeline that streamlines the software development lifecycle, from code commit to production deployment in seconds (or in some cases, minutes). - -Implementing CI/CD involves using various tools and practices. Jenkins, GitLab CI, CircleCI, and Travis CI are popular options for setting up CI pipelines, while tools like Spinnaker and Argo CD help with CD. - -## 3\. Infrastructure as Code (IaC) - -![Infrastructure as Code](https://assets.roadmap.sh/guest/infrastructure-as-code-w965a.png) - -Infrastructure as Code (IaC) is a game-changer in the DevOps world. Traditionally, provisioning infrastructure involved manual setup and configuration, which was time-consuming and, of course, prone to human error. IaC changes the game by treating infrastructure the same way we treat application code: as a set of scripts or configurations that can be version-controlled, tested, and automated. - -Through IaC, DevOps teams can ensure consistency and repeatability across different environments. It eliminates the "works on my machine" problem by providing a standardized environment for software to run on, whether it's on a developer's local machine, in a staging environment, or in production. - -Over the years, IaC tools have evolved quite a lot. At the start of it all, players like Chef and Puppet introduced the concept of configuration management, allowing you to define the desired state of your systems. Ansible took that one step further with its agentless architecture, making it easier to manage infrastructure at scale. Terraform took IaC to the next level by providing a tool-agnostic way to provision resources across multiple cloud providers, making it a favorite among DevOps engineers. - -## 4\. Containerization - -![Containerization](https://assets.roadmap.sh/guest/containers-docker-g491z.png) - -Containerization is a core practice and one of the main devops principles to constantly apply. Containers provide a lightweight, portable way to package software along with its dependencies, ensuring that it runs consistently across different environments. Containers share the host system's kernel, making them more efficient and faster to start up than virtual machines. - -These “containers” have been playing a key role in solving one of the age-old problems in software development: environment inconsistencies. By encapsulating an application and its dependencies into a container, you can ensure that it runs the same way on a developer's laptop as it does in production. This consistency simplifies the development process and reduces the risk of environment-related problems. - -In this space, Docker is the most popular tool for creating and managing containers (although not the only one), offering a simple way to build, package, and distribute containerized applications. Kubernetes takes containerization to the next level by providing a platform for orchestrating containers at scale. With Kubernetes, you can automate the deployment, scaling, and management of containerized applications, making it easier to manage complex, multi-container applications. - -While there is no excuse to not use DevOps at this stage in any software project, some of the biggest benefits of using containers in the DevOps lifecycle are: consistency, scalability, and portability. - -In other words, they make it easier to move applications between different environments. They also enable more efficient use of resources, as multiple containers can run on the same host without the overhead of a full virtual machine. - -## 5\. Monitoring and Observability - -![Monitoring and Observability](https://assets.roadmap.sh/guest/monitoring-servers-14k80.png) - -Monitoring and observability are essential components of the DevOps practice and key principles for any DevOps team. While monitoring focuses on tracking the health and performance of your systems, observability goes a step further by providing insights into the internal state of your applications based on the data they produce. Together, they enable DevOps teams to detect and troubleshoot issues quickly, ensuring that applications run smoothly. - -Continuous monitoring involves constantly tracking key metrics such as CPU usage, memory consumption, response times, and error rates. Tools like Prometheus, Grafana, and the ELK Stack (Elastic, Logstash, Kibana) are popular choices for collecting and visualizing this data. All public cloud providers also have their own solutions, in some cases even based on the open-source versions mentioned before. - -Whatever the tool of your choice is, they all provide real-time insights into the performance of your applications and infrastructure, helping you identify potential issues before they impact users. - -Now the practice of observability extends beyond monitoring by providing a deeper understanding of how your systems are behaving. It involves collecting and analyzing logs, metrics and traces to gain insights into the root cause of issues. OpenTelemetry, for instance, is an emerging standard for collecting telemetry data, offering a unified way to instrument, collect, and export data for analysis. This standardization makes it easier to integrate observability into your DevOps practices, regardless of the tools you're using. - -## 6\. Security in DevOps - -![Security in DevOps](https://assets.roadmap.sh/guest/secured-servers-amsed.png) - -Security is a critical aspect of the DevOps lifecycle, and it's something that needs to be integrated from the very beginning of any project expected to see the light of production at one point. - -DevSecOps is the practice of embedding security into the DevOps pipeline, ensuring that security measures are applied consistently throughout the software development lifecycle (reviewing code for vulnerabilities, checking IaC scripts, etc). Through this practice, DevOps helps catch vulnerabilities early and greatly reduce the risk of security breaches in production. - -Sadly, in many companies and teams that follow more traditional practices, security tends to be an afterthought, gaining importance only after the code is written and deployed. This approach can lead to costly and time-consuming fixes. DevSecOps, on the other hand, integrates security into every stage of the development and operations process, from code development to deployment. In the end, this helps security teams to automate security testing, identify vulnerabilities early, and enforce security policies consistently. All without having to read a single line of code themselves. - -In this space, tools like Snyk, Aqua Security, and HashiCorp Vault are king and they can help you integrate security into your DevOps workflows. - -## 7\. Reducing Toil and Technical Debt - -Toil and technical debt are two of the biggest productivity killers in software development. Toil refers to the repetitive, manual tasks that don't add direct value to the product, while technical debt is the accumulation of shortcuts and workarounds that make the codebase harder to maintain over time. Both can slow down your development workflow and make it more challenging to deliver new features. - -And because of that, one of the big and important DevOps principles is to aim to reduce both. Yes, DevOps teams can also help reduce technical debt. - -Reducing toil involves automating repetitive tasks to free up time for more valuable work. Tools like Ansible, Chef, and Puppet can help automate infrastructure management, while CI/CD pipelines can automate the build, test, and deployment processes. In the end, less manual work translates to reducing the chances of errors and giving team members the chance to focus on more interesting and strategic tasks. - -Technical debt, on the other hand, requires a proactive approach to address. It's about finding the right balance between delivering new features and maintaining the quality of the codebase. Regularly refactoring code, improving documentation, and addressing known issues can help keep technical debt in check. Of course, this also needs to be balanced with their ability to deliver new features and move the product forward. - -## 8\. GitOps: The Future of Deployment - -![GitOps](https://assets.roadmap.sh/guest/git-ops-tmggz.png) - -GitOps is a new practice that takes the principles of Git and applies them to operations. It's about using Git as the single source of truth for your infrastructure and application configurations. By storing everything in Git, you can use version control to manage changes, track history, and facilitate collaboration among development and operations teams. - -You essentially version your entire infrastructure with the same tools you version your code. - -In other words, all changes to the infrastructure and applications are made through pull requests to the Git repository. Once a change is merged, an automated process applies the change to the target environment. This approach provides a consistent, auditable, and repeatable way to manage deployments, making it easier to maintain the desired state of your systems. - -Through GitOps, teams can manage deployments and gain the following benefits: improved visibility, version control, and traceability. - -This methodology aligns well with the DevOps principles of automation, consistency, and collaboration, making it easier to manage complex deployments at scale. - -Key tools for implementing GitOps include Argo CD and Flux. These tools help you automate the deployment process by monitoring the Git repository for changes and applying them to the cluster. - -## 9\. Continuous Learning and Improvement - -![Continuous Learning and Improvement](https://assets.roadmap.sh/guest/learn-improve-4fzcr.png) - -In general the world of tech is constantly evolving and changing and continuous learning and improvement are essential practices for staying ahead and relevant. - -That said, in the DevOps landscape change is also a constant, with new tools, practices, and technologies emerging all the time. If you think about it, before 2006 we didn’t even have containers. - -So to keep up, DevOps engineers and teams need to be committed to learning and improving continuously. - -Encouraging a culture of continuous learning within your team can help keep everyone up-to-date with the latest DevOps trends and tools. This can include participating in conferences, attending workshops, and enrolling in online courses. Reading books like "The Phoenix Project," "The Unicorn Project," and "The DevOps Handbook" can provide valuable insights and inspiration. - -If you’re not into books, then websites like [12factor.net](http://12factor.net), [OpenGitOps.dev](http://OpenGitOps.dev), and [CNCF.io](http://CNCF.io) are also great resources for staying current with industry [best practices](https://roadmap.sh/devops/best-practices). - -Continuous improvement goes hand-in-hand with continuous learning. It's about regularly reviewing and refining your processes, identifying areas for improvement after failing and experimenting with new approaches. This iterative approach helps you optimize the development process, improve collaboration between devs and operations, and deliver better software. - -## 10\. Understanding Programming Concepts - -![Understanding Programming Concepts](https://assets.roadmap.sh/guest/code-sample-fjblw.png) - -While not every DevOps engineer needs to be a full-fledged developer, having a solid understanding of programming concepts is key to success in the professional world. - -A good grasp of programming helps bridge the gap between development and operations, making it easier to collaborate and understand each other's needs. Which, if you think about it, is literally the core principle of the DevOps practice. - -Understanding programming translates to being able to write scripts in languages like Bash, Python, or PowerShell to automate tasks, manage infrastructure, and interact with APIs. This can range from simple tasks like automating server setup to more complex operations like orchestrating CI/CD pipelines. - -Understanding programming concepts also enables you to better manage the software development lifecycle. It helps you understand how code changes affect system performance, security, and stability. This insight allows you to make more informed decisions when designing and implementing infrastructure and deployment processes. - -## 11\. Automation in DevOps - -[Automation](https://roadmap.sh/devops/automation) is at the heart of DevOps principles. It's about automating repetitive and manual tasks to accelerate processes, reduce errors, and free up time for more strategic work. We partially covered this concept before as part of the toil reduction principle. - -However, it’s important to explain that automation not only involves code builds and tests, it also includes infrastructure provisioning and application deployment. In other words, automation plays a key role in every stage of the DevOps lifecycle. - -The whole point of automation is to accelerate processes. It enables faster, more consistent, and more reliable software delivery. By automating tasks like code integration, testing, and deployment, you can reduce the time it takes to get new features into production and minimize the risk of human error. - -There are many areas in the DevOps lifecycle where automation can be applied, in fact, the challenge would be to find areas where it wouldn’t make sense to apply it. These include CI/CD pipelines, infrastructure provisioning, configuration management, monitoring, and security testing. In this area, some of the most popular [DevOps tools](https://roadmap.sh/devops/tools) are Jenkins, Ansible, Terraform, and Selenium. They all provide the building blocks for automating these tasks, allowing you to create a seamless and efficient development workflow that everyone enjoys. - -If you’re looking to start implementing automation in your DevOps workflow, consider starting small and gradually expanding automation efforts, using version control for automation scripts (Git is a great option), and continuously monitoring and refining automated processes. - -It's important to find a balance between automation and human intervention, ensuring that automation enhances the development workflow without introducing unnecessary complexity. - -## Conclusion - -And there you have it—the core principles and practices of DevOps in a nutshell. By mastering them, you'll be well on your way to becoming a great [DevOps engineer](https://roadmap.sh/devops/devops-engineer). - -Whether you're just starting out or looking to level up your DevOps game, there's always something new to learn and explore. So keep experimenting, keep learning, and most importantly, keep having fun\! - -After all, DevOps isn't just about making systems run smoothly—it's about building a culture that encourages innovation, collaboration, and growth. As you dive deeper into the DevOps practice, you'll not only become more skilled but also contribute to creating better software and more agile teams. diff --git a/src/data/guides/devops-shift-left-testing.md b/src/data/guides/devops-shift-left-testing.md deleted file mode 100644 index 7f60499db..000000000 --- a/src/data/guides/devops-shift-left-testing.md +++ /dev/null @@ -1,149 +0,0 @@ ---- -title: 'Why Does DevOps Recommend Shift-Left Testing Principles?' -description: 'Understand why DevOps emphasizes shift-left testing to boost early bug detection, reduce costs, and improve release cycles.' -authorId: william -excludedBySlug: '/devops/shift-left-testing' -seo: - title: 'Why Does DevOps Recommend Shift-Left Testing Principles?' - description: 'Understand why DevOps emphasizes shift-left testing to boost early bug detection, reduce costs, and improve release cycles.' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-shift-left-testing-16zah.jpg' -relatedGuidesTitle: 'Other Guides' -relatedGuidesId: devops -isNew: false -type: 'textual' -date: 2024-11-04 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Why shift-left testing is recommended in DevOps](https://assets.roadmap.sh/guest/devops-shift-left-testing-16zah.jpg) - -Why is there so much debate about testing methodologies if DevOps is about streamlining software delivery? Why do we still encounter approaches like shift-left, shift-right, and even shift-down testing? Shouldn’t there be one universally accepted strategy? - -[DevOps](https://roadmap.sh/devops) emphasizes rapid development cycles, continuous integration, and delivery, but your projects or teams may have different needs, goals, or challenges. Factors like the **type of application, infrastructure, team size, legacy systems**, and **release schedules** can lead you to choose different testing strategies. - -Despite these varying needs and challenges, DevOps recommends shift-left testing principles because it addresses the core problem of catching issues early. By integrating tests early, issues are resolved quickly before they become more costly and complex to fix. - -In this guide, you’ll learn what Shift-Left testing is, its benefits, things to consider, and best practices when implementing it. - -## What is Shift-Left testing? - -Shift-Left testing is a [principle in DevOps](https://roadmap.sh/devops/principles) that involves incorporating quality assurance and other testing activities **earlier** in the software development lifecycle (SDLC). - -Over the years, testing has been somewhat considered an afterthought of the development process. The traditional approach waits for the development cycle to end or at least waits until 50% of the development process is done before testing the application. This approach can be slow, costly, and does not maximize resources because bugs found during the tests can’t be easily fixed during the development cycle. - -![Shift-Left testing](https://assets.roadmap.sh/guest/shift-left-testing-devops-3cr7m.png) - -The main idea is to move testing to the **left side** of the development process so that it can happen earlier and more often during the design and development phase. - -Shift-Left testing aligns with the DevOps principle of continuous integration and continuous delivery (CI/CD) because [automated tests](https://roadmap.sh/devops/test-automation) can be written alongside the code and executed as part of the development pipeline. This approach ensures that issues are caught early, developers receive immediate feedback, and overall software quality is improved. - -To implement Shift-Left testing, organizations often rely on a variety of automated testing tools. While the choice of tools may vary based on team preference and specific projects, below are some popular tools for performing Shift-Left testing: - -- Jenkins -- Selenium -- Cucumber -- SonarCube -- JMeter - -## Benefits of Shift-Left testing in DevOps - -Let’s explore some of the reasons why Shift-Left is important in your DevOps processes: - -![Benefits of Shift-Left](https://assets.roadmap.sh/guest/shift-left-testing-devops-benefits-celh5.png) - -1. **Reduced cost**: A goal of every organization is to reduce its *operating expenses* and maximize profit. When Shift-Left testing is factored into your development process, bugs are identified and fixed early, which is far less expensive than when you address them after deployment. This approach saves you both time and resources as reworks are minimal. -2. **Faster feedback and early detection**: Shift-Left testing provides faster feedback on the quality of your code because you’re testing early in the development process. This means you and your team can catch bugs and detect issues before they escalate. Additionally, it reduces the likelihood of finding and fixing defects later in development or in production. -3. **Improved quality**: The overall experience of your application becomes more reliable and stable because you'll likely find and fix bugs earlier before they impact your users' experience. -4. **Faster time to market:** Because defects are reduced and the development process is optimized, you can iterate faster and continuously release software. -5. **Improved collaboration and continuous learning:** Shift-Left testing follows the DevOps principle of collaboration between developers, testers, and other stakeholders. This means the team has a sense of responsibility and ownership, and they’ll learn more from one another. - -Shift-Left testing advocates for testing earlier, moving the focus to the **left side** of the development process. You might wonder if this means testing ends after the design and development stages or if there's something like **right-side** testing as you prepare to go live. - -Well, you guessed right, there's indeed Shift-Right testing. Let’s explore that next. - -## What is Shift-Right testing? - -Shift-Right testing is a principle in DevOps that involves incorporating quality assurance and other testing activities **later** in the software development lifecycle (SDLC). This is usually done when software has been released or is being used in production. - -Unlike Shift-Left testing, which occurs at the beginning of the development process, Shift-Right testing occurs after the application has been deployed into production. It involves: - -- Closely monitoring the application’s performance in the production environment to identify bugs and issues. -- Gradually releasing new features to selected users to test their impact first before doing a full rollout. -- Collecting feedback from users to understand the overall users’ experience and identify areas of continuous improvement. -- Conducting A/B testing to compare different versions of the software or features to determine users’ behavior and outcomes. - -![Shift-Right testing](https://assets.roadmap.sh/guest/shift-right-testing-v86zs.png) - -The main idea is to move system testing to the **right side** of the development process, ensure that the application performs well in real-world scenarios, and catch issues that may not be apparent during Shift-Left testing. - -While Shift-Right testing comes with its own benefits when dealing with production-specific issues, it can be risky. The approach of fixing bugs in production can lead to downtime, cause a negative user experience, and damage your organization’s reputation. - -## Challenges in adopting Shift-Left testing - -It’s important to understand that Shift-Left testing is not a “magic wand” that solves all your testing problems. It also comes with its own challenges. Below are some challenges you might encounter when adopting it: - -1. **Required skills**: Developers, testers, and other stakeholders may need to acquire new skills like test [automation](https://roadmap.sh/devops/automation), continuous integration, and more. Training can be challenging for teams with limited resources. -2. **Cultural shift**: Adopting continuous testing on the left side of the development process requires a cultural change for all the stakeholders. Developers may need to take on additional testing responsibilities, while testers may need to acquire new skills. This can lead to resistance, adding to their workload and responsibilities. -3. **Cost implication**: The implementation process requires new toolsets and automation frameworks, which can be time-consuming and costly to set up. Additionally, teams must overcome the learning curve associated with these tools. -4. **Integration complexity**: Integrating testing in the early stage of SDLC can be complex, particularly in legacy and large systems. This is because it requires that all team members are aligned and willing to adjust their roles and responsibilities to accommodate the new testing approach. -5. **Risk management**: Testing early means teams must develop strategies to mitigate the risks associated with early testing and find a balance between early testing and potential risks. - -## Overcoming challenges and implementing shift-left testing - -Adopting Shift-Left testing in your development process comes with several challenges, particularly related to resources and cultural resistance, which may overshadow the advantages it offers. To successfully implement Shift-Left testing, consider the following strategies: - -- **Promote a testing-first mindset** and encourage cross-functional collaboration between your teams with an emphasis on shared ownership of the product. -- **Invest in training and skill development** focused on test automation, CI/CD, and other [best practices in DevOps](https://roadmap.sh/devops/best-practices). -- **Choose [DevOps tools](https://roadmap.sh/devops/tools) that align with your organization’s needs** and integrate seamlessly within your budget. -- **Start small** with a feature or pilot project, then gradually scale across the entire product chain. This approach will give your team the flexibility and the time to learn and refine strategies. -- **Regularly conduct risk assessments** to identify areas for improvement and implement corresponding mitigation strategies. - -## Best practices for implementing Shift-Left testing principles - -Below are some best practices to consider: - -![Shift-Left best practices](https://assets.roadmap.sh/guest/shift-left-testing-devops-principles-0yrp4.png) - -1. **Adopt early test plans**: Create a plan that clearly outlines the testing goals and scope of the project. Also, ensure that testing is integrated into the early stages of your SDLC. -2. **Test automation**: Use test [automation tools](https://roadmap.sh/devops/automation-tools) to automate unit tests, integration tests, functional tests, and other necessary test cases. Additionally, regularly update and maintain test scripts to keep them relevant and accurate when running CI/CD pipeline. -3. **Collaboration and communication**: Use project management tools like Jira and Linear, along with traditional methods such as meetings, check-ins, and stand-ups, to facilitate communication and collaboration between developers, testers, and other stakeholders. -4. **Continuous learning**: Encourage your team to keep up-to-date with the latest testing techniques and tools, participate in industry events to learn from experts, and share knowledge and best practices within the team. - -## Shift-Left testing in the real world - -To gain a better understanding of how teams are using Shift-Left testing to build better applications, let’s explore two real-world examples: Microsoft and Uber. - -### Microsoft - -Over two and a half years, a Microsoft team successfully replaced 27,000 legacy tests with modern DevOps unit tests and a shift-left process. This approach allowed them to enhance software quality and performance goals. - -![Microsoft, 2022. Pull request and rolling CI pipeline in action. https://learn.microsoft.com/en-us/devops/develop/shift-left-make-testing-fast-reliable#getting-faster](https://assets.roadmap.sh/guest/pr-rolling-ci-pipeline-jv1rp.png) - -Additionally, the Azure DevOps platform integrates various Shift-Left testing practices, which the team leverages to deliver reliable cloud services and tools to its customers. At the core of Shift-Left testing in Microsoft are: - -- Writing tests at the lowest possible level. -- Writing functional tests that can run anywhere. -- Treating test code as product code. - -### Uber - -Uber’s adoption of the Shift-Left testing principle of putting the test at the forefront of its software development process has helped it minimize risk and ensure the reliability and safety of its ride-sharing platform. - -![Uber, 2024. Separation of testing data. https://www.uber.com/en-GB/blog/shifting-e2e-testing-left/](https://assets.roadmap.sh/guest/shift-left-testing-devops-uber-0xk2a.png) - -One major change Uber made was to ensure they could test without deploying to production. This process requires launching a backend integration testing strategy **(BITS)** that enables on-demand continuous deployment and routing to test sandboxes. At the core of BITS are: - -- Isolating data between production and test environment. -- Using [Cadence](https://cadenceworkflow.io/?uclick_id=b36cfaa6-c7d0-4da0-a756-64e7e4c3466e), an open-source workflow engine, to define workflow state, retries, and timers for resource teardown. - -## Key takeaways - -As businesses evolve, they will bring new challenges and use cases that need to be solved. This means as a DevOps engineer, you need to factor in tests early into the development process to maintain a balance between product quality and speed. Additionally, you need to stay up to date with the latest DevOps trends and toolchains that will help you build reliable applications. - -Use the [DevOps roadmap](https://roadmap.sh/devops) to stay up to date with the latest developments and extensions in the DevOps ecosystem. diff --git a/src/data/guides/devops-skills.md b/src/data/guides/devops-skills.md deleted file mode 100644 index d07bcf119..000000000 --- a/src/data/guides/devops-skills.md +++ /dev/null @@ -1,475 +0,0 @@ ---- -title: '10+ In-Demand DevOps Engineer Skills to Master' -description: 'Find out exactly what it takes to be a successful DevOps engineer with my recommendations for essential DevOps skills' -authorId: fernando -excludedBySlug: '/devops/skills' -seo: - title: '10+ In-Demand DevOps Engineer Skills to Master' - description: 'Find out exactly what it takes to be a successful DevOps engineer with my recommendations for essential DevOps skills' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-engineer-skills-tlace.jpg' -relatedGuidesTitle: 'Other Guides' -relatedGuidesId: devops -isNew: false -type: 'textual' -date: 2024-09-12 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Our top 10 DevOps engineer skills](https://assets.roadmap.sh/guest/devops-engineer-skills-tlace.jpg) - -The role of the DevOps engineer is not always very well defined; some companies see it as the old-school sysadmin whose sole purpose is to take care of the platform's infrastructure. Others see it as the person in charge of the Terraform configuration files. In the end, properly understanding what DevOps is and what you should expect from this role is critical to properly taking advantage of it and adding the value it’s meant to be adding to your company. - -While you can work on becoming a DevOps engineer from scratch (there is actually a [DevOps roadmap](https://roadmap.sh/devops) for that), usually, a [DevOps engineer](https://roadmap.sh/devops/devops-engineer) is someone who has spent enough years either as a developer or in an operations role and wants to start helping solve the problems they’ve experienced throughout their entire career. This person sits between both sides and has intimate knowledge of one of them and a great deal of knowledge about the other side. - -With that said, understanding everything there is to know to become a DevOps engineer who excels at their job is not trivial, and that’s why in this article, we’re going to cover the top 10 DevOps skills to help you level up your [career in DevOps](https://roadmap.sh/devops/career-path). - -The top 10 DevOps engineer skills to master are: - -1. Understanding Linux and some scripting languages. -2. Knowing how to set up your CI/CD pipelines. -3. Embracing containerization and orchestration. -4. Learning about Infrastructure as Code. -5. Understanding cloud computing. -6. Knowing how to monitor your infrastructure and manage your logs. -7. Having a good grasp of security practices and tools. -8. Know how to set up your networking and what that entails for your infrastructure. -9. Knowing about version control. -10. And finally, understanding configuration management. - -Now, let’s get started. - -## 1\. Proficiency in Linux and Scripting - -![Bash code in terminal](https://assets.roadmap.sh/guest/bash-code-in-terminal-zj6j1.png) - -Linux is one of the most common operating systems in the world of software development because of its incredible support, performance, and flexibility, which makes mastering it one of the main DevOps skills to work on. - -Granted, the word “master” is loaded and there are many aspects of the OS that you don’t really need to worry about these days (with all the containers and IaC tools around), however without pushing yourself too hard and becoming a full-blown developer, investing part of your time into learning one or more scripting languages is definitely a good call. - -As a DevOps engineer, you will be scripting and automating tasks, so pick a couple of popular scripting languages and make sure you understand them enough to get the job done. For example, picking Bash is a safe bet, as Bash is the native scripting language in most Linux distros. On top of that, you can pick something like Python or Ruby; both are great options. With an English-like syntax that’s very easy to read and understand and a set of very powerful [DevOps automation](https://roadmap.sh/devops/automation) libraries and tools available, you should be more than fine. For example, if you’re picking Python, you’ll be able to work on Ansible playbooks or custom modules, and if you go with Ruby, you can write Chef cookbooks. - -In the end, it’s either about your own preference or the company’s standards if there are any, just pick one and figure out the tools at your disposal. - -## 2\. Understanding of Continuous Integration and Continuous Deployment (CI/CD) - -![continuous development vs continuous integration](https://assets.roadmap.sh/guest/continous-development-vs-continuous-integration-l2fak.png) - -Continuous Integration and Continuous Deployment (CI/CD) form the backbone of a successful DevOps methodology. As a DevOps engineer, mastering CI/CD is non-negotiable. - -### Understanding CI/CD - -At its core, Continuous Integration (CI) is about automatically integrating code changes from multiple contributors into a shared repository as many times a day as needed (which can be one, zero, or hundreds; the number should be irrelevant). - -The idea is to catch and fix integration bugs early and often, which is crucial for maintaining the health of your project. - -On the other hand, Continuous Deployment (CD) takes this a step further by automatically deploying the integrated code to production environments once it passes all necessary tests. Together, both practices minimize manual intervention, reducing errors and allowing for rapid and reliable delivery of software. - -### Key Tools for CI/CD - -To effectively implement CI/CD pipelines, you'll need to be proficient with the tools that make it possible. There are tons of them out there; some of the most common (and arguably, best ones) are: - -* **Jenkins**: An open-source automation server, Jenkins is highly customizable and supports a huge number of integration plugins. -* **GitLab CI**: Part of the larger GitLab platform, GitLab CI is tightly integrated with GitLab's version control and issue-tracking features. -* **CircleCI**: Known for its speed and simplicity, CircleCI is perfect for environments that prioritize cloud-native solutions. It provides a user-friendly interface and integrates well with popular tools like Docker, AWS, and Kubernetes. -* **GitHub Actions**: GitHub Actions is a powerful CI/CD tool integrated directly into GitHub, allowing developers to automate, test, and deploy their code right from their repositories. It supports custom workflows, integration with [other DevOps tools](https://roadmap.sh/devops/tools), and provides flexibility to run complex automation tasks across multiple environments. - -### Best Practices for Setting Up and Managing CI/CD Pipelines - -Setting up and managing CI/CD pipelines isn’t just about getting the tools to work; it’s about ensuring that they work well (whatever “well” means for your specific context). - -Here are some best practices to follow: - -1. **Start Small and Iterate**: Don’t try to automate everything at once. Start with the most critical parts of your workflow, then gradually expand the pipeline. -2. **Ensure Fast Feedback**: The faster your CI/CD pipeline can provide feedback, the quicker your team can address issues. -3. **Maintain a Stable Master Branch**: Always keep your master branch in a deployable state. Implement branch protection rules and require code reviews and automated tests to pass before any changes are merged. -4. **Automate Everything Possible**: From testing to deployment, automate as many steps in your pipeline as possible. -5. **Monitor and Optimize**: Continuously monitor your CI/CD pipelines for performance bottlenecks, failures, and inefficiencies. Use this data to refine your process. -6. **Security Considerations**: Integrate security checks into your CI/CD pipelines to catch vulnerabilities early. Tools like static code analysis, dependency checking, and container scanning can help ensure that your code is secure before it reaches production. - -## 3\. Containerization and Orchestration - -These technologies are at the heart of modern DevOps practices, enabling scalability, portability, and efficiency. - -### Basics of Containerization - -Containerization is a method of packaging applications and their dependencies into isolated units called containers. Unlike traditional virtual machines, which require a full operating system, containers share the host OS’s kernel while running isolated user spaces. - -This makes containers not only lightweight but also faster to start, and more resource-efficient. - -There are many benefits to this technology, as you can probably glean by now, but the primary benefits include: - -* **Portability**: Containers encapsulate everything an application needs to run, making it easy to move them across different environments. As long as there is a way to run containers in an OS, then your code can run on it. -* **Scalability**: Containers can be easily scaled up or down based on demand. This flexibility is crucial when you need to handle dynamic workloads. -* **Consistency**: By using containers, you can ensure that your applications run the same way across different environments, reducing the infamous "it works on my machine" problem. -* **Isolation**: With container applications, don’t use resources outside of the ones defined for them. This means each application is isolated from others running on the same host server, avoiding interference. - -### Key Containerization Tools - -When it comes to containerization tools, Docker is the most popular and widely adopted alternative. However, other tools like Podman are also gaining traction, especially in environments that prioritize security and compatibility with Kubernetes. - -Both tools offer robust features for managing containers, but the choice between them often comes down to specific use cases, security requirements, and integration with other tools in your DevOps toolkit. - -### Orchestration Tools - -While containerization simplifies application deployment, managing containers at scale requires something else: orchestration. - -Orchestration tools like Kubernetes and Docker Swarm automate the deployment, scaling, and management of containerized applications, ensuring that they run efficiently and reliably across distributed environments. - -* **Kubernetes**: Kubernetes is the de facto standard for container orchestration. Kubernetes provides a comprehensive platform for automating the deployment, scaling, and operation of containerized applications by managing clusters of containers. -* **Docker Swarm**: Docker Swarm is Docker’s native clustering and orchestration tool. It’s simpler to set up and use compared to Kubernetes, making it a good choice for smaller teams or less complex projects. - -## 4\. Infrastructure as Code (IaC) - -Infrastructure as Code (IaC) has become a foundational practice for DevOps teams. IaC allows you to manage and provision your infrastructure through code, offering a level of automation and consistency that manual processes simply can’t match. - -### Importance of IaC in Modern DevOps Practices - -IaC is crucial in modern DevOps because it brings consistency, scalability, and speed to infrastructure. - -IaC allows teams to define their infrastructure in code, which can be versioned, reviewed, and tested just like application code. If you think about it, IaC is the perfect example of what DevOps means: the merger of both worlds to achieve something that is greater than the sum of its parts. - -Nowadays, IaC is not just a “best practice” but rather, an indispensable part of a DevOps engineer’s workflow, and here is why: - -* **Consistency Across Environments**: As we’ve already mentioned, with IaC, you can ensure that your environments are all configured exactly the same way. -* **Scalability**: Whether you need to add more servers, databases, or other resources, you can do it quickly and reliably by updating your code and reapplying it. -* **Version Control and Collaboration**: By storing your infrastructure configurations in a version control system like Git, you enable better collaboration and control. -* **Automation and Efficiency**: Once your infrastructure is defined in code, you can automate the provisioning, updating, and teardown of resources, allowing your team to focus on higher-value tasks. - -### Key IaC Tools: Terraform, Ansible, Chef, Puppet - -Several tools have become go-to solutions for IaC, each offering unique strengths. - -Here are some of the most popular ones; however, feel free to pick others if they fit better in your particular use case/context: - -* **Terraform**: Terraform is one of the most widely used IaC tools. It’s cloud-agnostic, meaning you can use it to manage infrastructure across different cloud providers like AWS, Azure, and Google Cloud. -* **Ansible**: While Ansible’s main focus is automating configuration tasks across multiple servers, it is capable of also working as an IaC tool by providing support for infrastructure provisioning, application deployment, and orchestration. -* **Chef**: Chef is another strong player in the configuration management space. It uses a Ruby-based language to create "recipes" that automate the deployment and management of infrastructure. -* **Puppet**: Puppet offers a solid solution for configuration management, using its own declarative language to define the state of your infrastructure. - -### Best Practices for Writing and Managing Infrastructure Code - -Like with any coding project, writing clean and easy-to-read code will help a great deal in making the project itself a success. That’s no different in the case of IaC, the words “clean code” need to be engraved in every DevOp’s mind. - -And this is what “clean” means in this context: - -1. **Modularize Your Code**: Break down your infrastructure code into smaller, reusable modules. This approach is especially useful for larger projects where the infrastructure files grow in number; this way you can reuse sections and simplify maintenance. -2. **Use Version Control**: Store all your infrastructure code in a version control system like Git. This practice enables you to track changes, collaborate with others, and roll back if something goes wrong. -3. **Test Your Infrastructure Code**: Just like application code, infrastructure code should be tested. Tools like Terraform provide validation for configurations, and frameworks like Inspec or Testinfra can verify that your infrastructure is working as expected after deployment. -4. **Follow the Principle of Least Privilege**: When defining infrastructure, ensure that each component has only the permissions it needs to perform its function. This practice reduces security risks by limiting the potential impact of a breach or misconfiguration. -5. **Keep Secrets Secure**: Avoid the rooky mistake of hardcoding sensitive information, such as API keys or passwords, directly into your infrastructure code. Use tools like HashiCorp Vault, AWS Secrets Manager, or Azure Key Vault to manage secrets securely. -6. **Document Your Code**: Just like application code, your infrastructure code should also be commented on and explained, not for you now, but for you next week or someone else next month. The easier it is to understand the code, the faster they’ll be able to work with it. -7. **Integrate IaC into CI/CD Pipelines**: Automate as much as possible in the IaC workflow. That includes the validation, testing, and deployment of your infrastructure code by integrating it into your continuous integration and continuous deployment (CI/CD) pipelines. This ensures that your environments are always up-to-date and consistent with your codebase, reducing the risk of drift. - -## 5\. Cloud Computing Expertise - -![cloud providers](https://assets.roadmap.sh/guest/cloud-providers-wb1f5.png) - -In the DevOps ecosystem, cloud computing is more than just a trend companies are starting to follow—it's a fundamental element that defines modern software development and deployment practices. - -And because of that, it’s one of the main DevOps skills you’ll want to develop. - -### Importance of Cloud Platforms in DevOps - -Cloud platforms have revolutionized the way software is developed, deployed, and managed. The cloud allows organizations to leverage vast computing resources on demand, scale their operations effortlessly, and reduce infrastructure costs. - -Specifically for DevOps teams, cloud platforms offer several key benefits: - -* **Scalability**: One of the most significant advantages of cloud computing is its ability to scale resources up or down based on demand. This elasticity is crucial for handling varying workloads, ensuring that applications remain responsive and, most importantly (as some would argue), cost-effective. -* **Speed and Agility**: Provisioning of resources can be done with just a few clicks, allowing DevOps teams to spin up development, testing, and production environments in minutes. This speed accelerates the software development lifecycle, enabling faster releases and more frequent updates. -* **Global Reach**: Cloud providers operate data centers around the world, making it easier for organizations to deploy applications closer to their users, reducing latency and improving performance. -* **Cost Efficiency**: This is a recurring topic when discussing cloud platforms, as they help reduce the need for large upfront capital investments in hardware. Instead, organizations can pay for the resources they use, optimizing costs and reducing waste. -* **Automation**: Cloud environments are highly automatable, allowing DevOps teams to automate infrastructure provisioning, scaling, and management. - -### Key Cloud Providers: AWS, Azure, Google Cloud Platform (GCP) - -When it comes to cloud providers, three providers dominate the market: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). While they’re not the only ones, and in some regions of the world, they’re not even the top providers. In general, they own most of the cloud market. - -Each of them offers a vast array of services and tools that cater to different needs, making them the go-to choices for DevOps professionals. - -* **Amazon Web Services (AWS)**: AWS is the largest and most mature of the three, offering an extensive range of services, including computing power (EC2), storage (S3), databases (RDS), and more. AWS is known for its large number of features, including advanced networking, security, and analytics tools. For DevOps engineers, AWS provides powerful services like AWS Lambda (serverless computing), AWS CodePipeline (CI/CD), and CloudFormation (IaC), which are essential for building and managing cloud-native applications. -* **Microsoft Azure**: Azure is a close competitor to AWS, particularly strong in enterprise environments where Microsoft technologies like Windows Server, SQL Server, and .NET are prevalent. Azure offers a very rich list of cloud services, including, like the other two, virtual machines, AI, and machine learning tools. Azure also offers DevOps-specific services like Azure DevOps, which integrates CI/CD, version control, and agile planning into a single platform. Azure's hybrid cloud capabilities also make it a popular choice for organizations that need to integrate on-premises infrastructure with cloud resources. -* **Google Cloud Platform (GCP)**: GCP, while newer to the cloud market compared to AWS and Azure, has quickly gained a reputation for its data analytics, machine learning, and container orchestration services. Google’s Kubernetes Engine (GKE), for instance, is quite liked by the DevOps community for managing containerized applications at scale. GCP is also known for its strong support of open-source technologies, making it a favorite among developers who prioritize flexibility and innovation. - -### Understanding Cloud-Native Tools, Services, and Architectural Patterns - -Another key technical skill for DevOps engineers is to understand not only cloud-native tools and services but also the architectural patterns that define modern application development. - -These patterns define how applications are structured and interact with cloud infrastructure, directly affecting areas such as scalability, resilience, and maintainability. - -* **Microservices Architecture**: In a microservices architecture, applications are composed of small, independent services that communicate over APIs. Key tools to understand by DevOps engineers include **API gateways** (like AWS API Gateway), **service meshes** (such as Istio), and **message queues** (like Amazon SQS or Google Pub/Sub). -* **Service-Oriented Architecture (SOA)**: SOA is a broader (and older) architectural style where services are designed to provide specific business functionalities and can communicate with each other over a network. Tools like **Enterprise Service Buses (ESBs)** and **message brokers** (such as RabbitMQ) are often used to facilitate SOA architectures. -* **Serverless Architecture**: Serverless computing allows developers to build and deploy applications without managing the underlying infrastructure. In a serverless architecture, code is executed in response to events, such as HTTP requests or changes in data, using services like **AWS Lambda**, **Azure Functions**, or **Google Cloud Functions**. -* **Event-Driven Architecture**: In an event-driven architecture, applications respond to events in real-time, often using tools like **event streams** (e.g., Apache Kafka) and **message queues**. - -## 6\. Monitoring and Logging - -![monitoring logging servers](https://assets.roadmap.sh/guest/monitoring-logging-servers-ztf1a.png) - -Monitoring and logging are vital components of a robust DevOps strategy. They provide visibility into the health and performance of your systems, allowing you to detect issues early, troubleshoot, and ensure the reliability of your applications. - -### Importance of Monitoring and Logging for Maintaining System Health - -No matter what type of application you’re running, maintaining the health and performance of your systems is crucial if your business depends on it. - -Monitoring and logging has turned into one of the most relevant DevOps skills out there. - -Through monitoring you can track the performance of your infrastructure and applications in real-time, alerting you to any potential problems such as resource bottlenecks, slowdowns, or outages. - -Logging, on the other hand, captures detailed records of system events and user interactions, providing invaluable information for diagnosing problems and understanding system behavior. - -The reasons why you want to have effective monitoring and logging, are: - -* **Proactive Issue Detection**: By continuously monitoring system metrics, you can detect issues before they escalate into critical problems, reducing downtime and improving overall system reliability. -* **Troubleshooting and Root Cause Analysis**: Logs provide detailed information about system events, making it easier to pinpoint the root cause of issues. This speeds up the resolution process and minimizes the impact on users. -* **Performance Optimization**: Monitoring allows you to track key performance indicators (KPIs) and identify areas where your systems can be optimized, leading to better resource utilization and cost savings. -* **Compliance and Auditing**: Logging is essential for maintaining compliance with regulatory requirements. Logs can be used to audit system access, track changes, and ensure that your systems meet security and operational standards. - -### Key Tools for Monitoring: Prometheus, Grafana, Nagios - -Several tools have become essential for monitoring systems in DevOps environments. Each offers unique features tailored to different needs, from real-time metrics collection to visual dashboards and alerting. - -* **Prometheus**: Prometheus is an open-source monitoring tool designed for reliability and scalability. It collects real-time metrics from your systems and applications, stores them in a time-series database, and supports powerful query languages for analysis. -* **Grafana**: Grafana is a popular open-source platform for visualizing monitoring data. It integrates with Prometheus and other data sources, allowing you to create interactive, customizable dashboards that provide insights into system performance at a glance. -* **Nagios**: Nagios is one of the oldest and most widely used monitoring tools. It provides comprehensive monitoring of network services, host resources, and infrastructure components. - -### Logging Tools: ELK Stack (Elasticsearch, Logstash, Kibana), Splunk - -Effective logging requires tools that can collect, store, and analyze large volumes of log data efficiently, given how much information modern systems can generate. - -The following tools are among the most widely used in the industry: - -* **ELK Stack**: The ELK Stack is a powerful open-source solution for managing logs. It consists of **Elastic** for storing and searching logs, **Logstash** to act as a data processing/ingestion pipeline, and **Kibana** for data visualization. -* **Splunk**: Splunk is a commercial tool that offers advanced log management and analysis capabilities. It can ingest data from a wide variety of sources, index it in real time, and provide powerful search and reporting features. - -### Best Practices for Setting Up Effective Monitoring and Logging Systems - -While both practices are crucial for a successful DevOps strategy, if you ignore best practices the results you’ll get will be subpar, at best. - -Instead, try to follow these (or some of them) guidelines to ensure you get the most out of your monitoring and logging efforts. - -1. **Define Clear Objectives**: Before setting up your monitoring and logging systems, define what you want to achieve. Identify the key metrics and logs that are most critical to your operations, such as CPU usage, memory consumption, application response times, and error rates. -2. **Implement Comprehensive Monitoring**: Monitor all layers of your infrastructure, from hardware and networks to applications and services. Use a combination of tools to ensure that no aspect of your system goes unmonitored. If you ignore one area, you’ll end up having blindspots when debugging and trying to troubleshoot problems. -3. **Centralize Log Management**: Centralizing your logs in a single platform like the ELK Stack or Splunk allows for easier management, search, and analysis. This centralization is particularly important in distributed systems where logs are generated across multiple servers and services. -4. **Set Up Alerts and Notifications**: Monitoring without alerting is like watching a movie without any sound; if you constantly pay attention to the picture, you might figure out what’s happening on a general level, but you’ll miss the details. And with monitoring, it’s the same thing: set up alerts and notifications so when a threshold is exceeded (say, the number of error responses in the last 10 minutes), you’ll know, even if it’s in the middle of the night. -5. **Ensure Scalability**: As your infrastructure grows, your monitoring and logging systems need to scale accordingly. Choose tools that can handle increasing volumes of data without compromising performance. In other words, don’t turn your logging/monitoring setup into a bottleneck for your platform. -6. **Regularly Review and Tune**: Continuously review and adjust your monitoring and logging configurations. As your systems evolve, your monitoring and logging needs may change, requiring you to add new metrics, refine alert thresholds, or optimize data retention policies. -7. **Secure Your Monitoring and Logging Infrastructure**: Protect your monitoring and logging data from unauthorized access. Ensure that logs containing sensitive information are encrypted and access to monitoring dashboards is restricted based on roles. - -## 7\. Security Practices and Tools (DevSecOps) - -As DevOps has transformed software development by integrating development and operations teams together into a seamless process, security can no longer be treated as an afterthought. The rise of DevSecOps emphasizes the need for DevOps engineers to develop their security skills. - -### Integrating Security into the DevOps Pipeline - -DevSecOps shifts the classical paradigm (having security reviews happen at the end of the development lifecycle) by integrating security into every phase of the DevOps pipeline—from code development to deployment and beyond. That, in turn, involves the following: - -* **Shift-Left Security**: This principle involves moving security practices earlier in the SDLC, such as during the coding and design phases. -* **Continuous Security**: Security checks should be continuous and automated throughout the pipeline. This ensures that each code change, build, and deployment is evaluated for security risks. -* **Collaboration and Culture**: DevSecOps is as much about culture as it is about tools. Developers, operations, and security teams must collaborate closely, sharing responsibility for security. - -### Key Security Practices - -To effectively integrate security into the DevOps pipeline, certain practices are essential: - -* **Automated Security Testing**: Automation is key to scaling security practices within a rapidly growing DevOps environment. Automated security testing involves integrating security checks into your CI/CD pipelines. This can include static application security testing (SAST) to analyze source code for security flaws, dynamic application security testing (DAST) to evaluate running applications, and interactive application security testing (IAST) that combines both approaches. -* **Vulnerability Scanning**: Regular vulnerability scanning is crucial for identifying and mitigating risks across your infrastructure and applications. Scanning tools can detect known vulnerabilities in code, dependencies, container images, and cloud configurations. -* **Security as Code**: Just as Infrastructure as Code (IaC) treats infrastructure configuration as code, Security as Code applies the same principles to security configurations. This involves automating the provisioning and management of security controls, policies, and compliance checks. - -### Tools for DevSecOps - -Several tools have emerged to support the integration of security into the DevOps practice. These tools help automate security tasks, identify vulnerabilities, and enforce security policies, making it easier for teams to adopt DevSecOps practices. - -Some examples are: - -* **Aqua Security**: Aqua Security specializes in securing cloud-native applications, particularly those that run in containers. Aqua provides a comprehensive platform for securing the entire container lifecycle, from development to runtime. -* **Snyk**: Snyk is a developer-friendly security platform that helps identify and fix vulnerabilities in open-source libraries, container images, and infrastructure as code. Snyk integrates with CI/CD pipelines, providing automated security testing and real-time feedback. -* **Trivy**: Trivy is an open-source vulnerability scanner that is particularly well-suited for container environments. It scans container images, file systems, and Git repositories for known vulnerabilities, misconfigurations, and secrets. - -## 8\. Networking and System Administration - -![list of linux processes](https://assets.roadmap.sh/guest/list-of-linux-processes-rnqzo.png) - -Networking and system administration are foundational DevOps skills. These disciplines ensure that the infrastructure supporting your applications is robust, secure, and efficient. - -### Which networking concepts are most relevant to DevOps? - -Networking is the backbone of any IT infrastructure, connecting systems, applications, and users. A solid understanding of networking concepts is crucial for DevOps engineers to design, deploy, and manage systems effectively. - -Some of the most important concepts include: - -* **TCP/IP Networking**: TCP/IP (Transmission Control Protocol/Internet Protocol) is the fundamental protocol suite for the Internet and most private networks. Understanding how TCP/IP works is essential. -* **Network Topologies**: Network topology refers to the arrangement of different elements (links, nodes, etc.) in a computer network. Common topologies include star, mesh, and hybrid configurations. -* **Load Balancing**: Load balancing is the process of distributing network or application traffic across multiple servers to ensure no single server becomes overwhelmed. DevOps engineers need to understand different load balancing algorithms (round-robin, least connections, IP hash) and how to implement load balancers (like NGINX, HAProxy, or cloud-native solutions). -* **Firewalls and Security Groups**: Firewalls are essential for controlling incoming and outgoing network traffic based on predetermined security rules. In cloud environments, security groups serve a similar function by acting as virtual firewalls for instances. -* **DNS (Domain Name System)**: DNS is the system that translates human-readable domain names (like [www.example.com](http://www.example.com)) into IP addresses that computers use to identify each other on the network. -* **VPNs and Secure Communication**: Virtual Private Networks (VPNs) allow secure communication over public networks by encrypting data between remote devices and the network. - -### System Administration Tasks and Best Practices - -System administration involves the management of computer systems, including servers, networks, and applications. DevOps engineers often take on system administration tasks to ensure that infrastructure is stable, secure, and performing optimally. - -Some of these tasks include: - -* **User and Permission Management**: Managing user accounts, groups, and permissions is fundamental to system security. -* **Server Configuration and Management**: Configuring servers to meet the needs of applications and ensuring they run efficiently is a core task. -* **System Monitoring and Maintenance**: As we’ve already mentioned, regular monitoring of system performance metrics is essential for proactive maintenance. -* **Backup and Recovery**: Regular backups of data and configurations are crucial for disaster recovery. -* **Patch Management**: Keeping systems up to date with the latest security patches and software updates is critical for maintaining your infrastructure secure. -* **Security Hardening**: Security hardening involves reducing the attack surface of a system by configuring systems securely, removing unnecessary services, and applying best practices. -* **Script Automation**: Developing your automation skills is key, as you’ll be automating routine tasks with scripts every day. Common scripting languages include Bash for Linux and PowerShell for Windows. - -### Best Practices for Networking and System Administration - -1. **Automate Repetitive Tasks**: Use automation tools and scripts to handle routine tasks such as backups, patch management, and monitoring setup. -2. **Implement Redundancy and Failover**: Design your network and systems with redundancy and failover mechanisms. This includes setting up redundant network paths, using load balancers, and configuring failover for critical services to minimize downtime. -3. **Enforce Strong Security Practices**: Regularly audit user access, apply patches promptly, and follow security best practices for hardening systems. -4. **Regularly Review and Update Documentation**: Keep detailed documentation of your network configurations, system setups, and processes. -5. **Monitor Proactively**: Set up comprehensive monitoring for all critical systems and networks. Alerts should be used to catch issues early, and logs should be reviewed regularly to spot potential security or performance issues. -6. **Test Disaster Recovery Plans**: Regularly test your backup and disaster recovery procedures to ensure they work as expected. - -## 9\. Familiarity with Version Control Systems - -Version control systems (VCS) are at the center of modern software development, enabling teams to collaborate, track changes, and manage their codebase. - -In a DevOps environment, where continuous integration and continuous deployment (CI/CD) are central practices, mastering version control is not just beneficial—it's essential. - -### Importance of Version Control in DevOps Workflows - -Version control is crucial in DevOps for several reasons: - -* **Collaboration**: Version control systems allow multiple developers to work on the same codebase simultaneously without overwriting each other's changes. -* **Change Tracking**: Every change to the codebase is tracked, with a history of who made the change, when, and why. -* **Branching and Merging**: Version control systems enable the creation of branches, allowing developers to work on new features, bug fixes, or experiments in isolation. -* **Continuous Integration/Continuous Deployment (CI/CD)**: Version control is crucial to CI/CD pipelines, where code changes are automatically tested, integrated, and deployed. -* **Disaster Recovery**: In case of errors or issues, version control allows you to revert to previous stable versions of the codebase, minimizing downtime and disruption. - -### Mastering Git: Key Commands, Workflows, and Best Practices - -Git is the most widely used version control system in the DevOps world, known for its flexibility, performance, and breadth of features. Having a deep understanding of Git is crucial for any DevOps engineer, as it is the foundation upon which most CI/CD workflows are built. - -The key commands you should try to master first are `init`, `clone`, `commit`, `pull`/`push`, `branch`, `checkout`, `merge`, and one that is definitely useful in your context: `log`. - -#### Git Workflows - -Git can be used as the driving force for your development workflow. However, there are many ways to use it. Some of the most common ones are: - -* **Feature Branch Workflow**: Developers create a new branch for each feature or bug fix. Once complete, the branch is merged back into the main branch, often through a pull request, where code reviews and automated tests are conducted. -* **Gitflow Workflow**: A more structured workflow that uses feature branches, a develop branch for integration, and a master branch for production-ready code. It also includes hotfix branches for urgent bug fixes in production. -* **Forking Workflow**: Common in open-source projects, this workflow involves forking a repository, making changes in the fork, and then submitting a pull request to the original repository for review and integration. - -#### Best practices when using Git - -* **Commit Often, Commit Early**: Make small, frequent commits with clear, descriptive messages. This practice makes it easier to track changes and revert specific parts of the codebase if needed. -* **Use Meaningful Branch Names**: Name branches based on the work they are doing, such as `feature/user-authentication` or `bugfix/login-issue`. -* **Perform Code Reviews**: Use pull requests and code reviews as part of the merge process. -* **Keep a Clean Commit History**: Use tools like `git rebase` to clean up your commit history before merging branches. A clean commit history makes it easier to understand the evolution of the project and debug issues. -* **Resolve Conflicts Early**: When working on shared branches, regularly pull changes from the remote repository to minimize and resolve merge conflicts as early as possible. - -### Tools for Managing Version Control: GitHub, GitLab, Bitbucket - -While Git itself is a command-line tool, various platforms provide user-friendly interfaces and additional features to manage Git repositories effectively. - -* **GitHub**: GitHub is the most popular platform for hosting Git repositories. It offers many collaboration features and on top of them, GitHub Actions integrates CI/CD directly into the platform, automating workflows from development to deployment. -* **GitLab**: GitLab is a complete DevOps platform that includes Git repository management, CI/CD, issue tracking, and more. GitLab can be hosted on-premises, which is a significant advantage for organizations with strict data security requirements. -* **Bitbucket**: Bitbucket, developed by Atlassian, integrates tightly with other Atlassian tools like Jira and Confluence. It supports Git and Mercurial and offers features like pull requests, code reviews, and CI/CD pipelines through Bitbucket Pipelines. - -As usual, the right set of DevOps tools will drastically depend on your specific context and needs. - -## 10\. Knowledge of Configuration Management - -Configuration management is a critical component of DevOps, enabling teams to automate the setup and maintenance of systems and applications across different environments. - -### The Role of Configuration Management in DevOps - -This practice involves maintaining the consistency of a system's performance and functionality by ensuring that its configurations are properly set up and managed. - -In DevOps, where continuous integration and continuous deployment (CI/CD) are key practices, understanding how to manage your configurations ensures that environments are consistently configured, regardless of where they are deployed. - -Configure once and deploy endless times; that’s the DevOps way. - -The main reasons why this is such an important practice in DevOps are: - -* **Automation tools**: These tools automate the process of setting up and maintaining infrastructure, reducing manual effort and minimizing the risk of human error. -* **Consistency Across Environments**: By defining configurations as code, conf. management ensures that all environments are configured identically. -* **Scalability**: As systems scale, manually managing configurations becomes impractical. Configuration management allows you to scale infrastructure and applications, ensuring that new instances are configured correctly from the start. -* **Compliance and Auditing**: These tools provide a clear and auditable record of system configurations. This is essential for compliance with industry standards and regulations. -* **Disaster Recovery**: In the event of a system failure, configuration management tools can quickly restore systems to their desired state, reducing downtime and minimizing the impact on business operations. - -### DevOps Configuration Management Tools to Master - -Several tools have become staples in this landscape, each offering unique features and strengths. For example: - -* **Ansible**: Ansible, developed by Red Hat, is an open-source tool known for its simplicity and ease of use. It uses YAML for configuration files, known as playbooks, which are easy to read and write. Ansible is ideal for automating tasks like software installation, service management, and configuration updates across multiple servers. -* **Puppet**: Puppet is a powerful tool that uses a declarative language to define system configurations. Puppet’s strength lies in its scalability and ability to manage large, complex environments. -* **Chef**: Chef is another popular tool that uses a Ruby-based DSL (Domain-Specific Language) to write recipes and cookbooks, which define how systems should be configured and managed. - -### Best Practices for Managing Configurations Across Environments - -Effective configuration management requires you to follow best practices that ensure consistency, reliability, and security across all environments. - -1. **Use Configuration as Code (CaC)**: Treat configurations as code by storing them in version control systems like Git. -2. **Modularize Configurations**: Break down configurations into reusable modules or roles. This approach allows you to apply the same configuration logic across different environments. -3. **Test Configurations in Multiple Environments**: Before deploying configurations to production, test them thoroughly in staging or testing environments. -4. **Implement Idempotency**: Ensure that your processes are idempotent, meaning that applying the same configuration multiple times does not change the system after the first application. -5. **Centralization**: Use a centralized tool to maintain a single source of truth for all configurations. -6. **Encrypt Sensitive Data**: When managing configurations that include sensitive data (e.g., passwords, API keys), use encryption and secure storage solutions like HashiCorp Vault. -7. **Document Configurations and Changes**: Maintain detailed documentation for your configurations and any changes made to them. -8. **Monitor and Audit Configurations**: Continuously monitor configurations to detect and prevent drift (when configurations deviate from the desired state). - -## Bonus: Collaboration and Communication Skills - -While technical skills are critical to becoming a successful DevOps engineer, the importance of soft skills—particularly collaboration and communication—cannot be ignored. - -In a DevOps environment, where cross-functional teams work closely together to deliver software, effective communication, and collaboration are essential for success. - -### Importance of Soft Skills in a DevOps Environment - -DevOps is not just about tools and processes; it's also about people and how they work together. - -Key reasons why soft skills are crucial in a DevOps environment: - -* **Cross-Functional Collaboration**: DevOps brings together diverse teams with different expertise—developers, operations, QA, security, and more. -* **Problem Solving and Conflict Resolution**: In software development in general, issues and conflicts are inevitable. Strong communication skills help teams navigate these challenges, finding quick resolutions and keeping the focus on delivering value to the customer. -* **Agility and Adaptability**: DevOps teams often need to adapt to changing requirements and priorities. Effective communication ensures that these changes are understood and implemented without issues. - -### Tools for Effective Collaboration: Slack, Microsoft Teams, Jira - -Several tools are essential for facilitating communication and collaboration in a DevOps environment. - -Is there an absolute best one? The answer to that question is “no.”, the best option depends on your needs and preferences, so study the list and figure out yourself which software (or combination of) helps your specific teams. - -* **Slack**: Slack is a popular communication platform designed for team collaboration. It offers integration with other DevOps tools like GitHub, Jenkins, and Jira. -* **Microsoft Teams**: Microsoft Teams is another powerful collaboration tool, especially popular in organizations using the Microsoft ecosystem. -* **Jira**: Jira, developed by Atlassian, is a robust project management tool that helps teams track work, manage backlogs, and plan sprints. It’s particularly useful in Agile and DevOps environments where transparency and continuous improvement are key. - -### Best Practices for Fostering a Collaborative Culture - -Building a collaborative culture in a DevOps environment requires effort and ongoing commitment. - -Here are some general guidelines you can follow to help achieve that collaborative environment: - -1. **Promote Open Communication**: Encourage team members to communicate openly about their work, challenges, and ideas. -2. **Regular Stand-Ups and Check-Ins**: Implement daily stand-ups or regular check-ins to ensure that everyone is on the same page. Whether they’re in person, during a video call, or asynchronous, these check-ins help find blockers and solve them fast. -3. **Use Collaborative Documentation**: Maintain shared documentation using tools like Confluence or Google Docs. -4. **Encourage Cross-Functional Training**: Facilitate training sessions or workshops where team members from different disciplines can learn about each other's work. -5. **Foster a Blameless Culture**: In a DevOps environment, mistakes and failures should be viewed as learning opportunities rather than reasons to assign blame. Encourage a blameless culture where issues are discussed openly, and the focus is on understanding the root cause. - -## Conclusion - -In the world of DevOps, mastering a diverse set of skills is not an option but rather an absolute must. From understanding the details of cloud computing and infrastructure as code to implementing monitoring and security practices, each skill plays a crucial role in fulfilling the main goal of any DevOps practice: enabling fast, reliable, and secure software delivery. - -For those looking to deepen their understanding or get started on their DevOps journey, here are some valuable resources: - -* [**Expanded DevOps Roadmap**](https://roadmap.sh/devops): A comprehensive guide that details the full range of DevOps skills, tools, and technologies you need to master as a DevOps engineer. -* [**Simplified DevOps Roadmap**](https://roadmap.sh/devops?r=devops-beginner): A more streamlined version that highlights the core components of a DevOps career, making it easier for beginners to navigate the field. -* [**DevOps interview questions**](https://roadmap.sh/questions/devops): Test your knowledge of DevOps by answering popular interview questions, and challenge yourself to level up and ace your next job interview. - -Success in DevOps is about cultivating a well-rounded skill set that combines technical expertise with strong collaboration, communication, and problem-solving abilities. - -As the industry continues to evolve, so too will the tools, practices, and challenges that DevOps engineers face. By committing to continuous learning and staying adaptable, you can ensure that you remain at the forefront of this dynamic field, driving innovation and delivering value in your organization. diff --git a/src/data/guides/devops-test-automation.md b/src/data/guides/devops-test-automation.md deleted file mode 100644 index f048defae..000000000 --- a/src/data/guides/devops-test-automation.md +++ /dev/null @@ -1,214 +0,0 @@ ---- -title: 'DevOps Test Automation: How to accelerate software delivery' -description: 'Want to deliver software faster? Dive into the power of DevOps test automation and transform your testing pipeline with proven techniques.' -authorId: fernando -excludedBySlug: '/devops/test-automation' -seo: - title: 'DevOps Test Automation: How to accelerate software delivery' - description: 'Want to deliver software faster? Dive into the power of DevOps test automation and transform your testing pipeline with proven techniques.' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-test-automation-nvpbi.jpg' -relatedGuidesTitle: 'Other Guides' -relatedGuidesId: devops -isNew: false -type: 'textual' -date: 2025-01-16 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![What is DevOps test automation?](https://assets.roadmap.sh/guest/devops-test-automation-nvpbi.jpg) - -Long gone are the days when putting a new feature into production meant 3 months of planning, coordination, and careful delivery. The industry has evolved into a new, faster, and more agile way of working. - -Instead of waiting for months before pushing something into production, companies are now able to do it multiple times a day, any day (even Fridays!). - -All of that is thanks to DevOps test automation, or in other words, thanks to the ability to include tests into the DevOps pipeline. That allows teams to confidently promote to production code that is tested and validated by a machine. - -How does that all work? Let’s dive in and I’ll explain it all. - -## What is DevOps Test Automation? - -DevOps test automation refers to the integration of automated testing into the DevOps pipeline to ensure that every piece of code is properly tested throughout the development lifecycle. It plays a vital role in achieving the speed and reliability that DevOps promises, helping teams deliver high-quality software faster. - -Test automation removes the human factor from the process (at least from most of it) and eliminates the bottlenecks caused by manual testing by running tests more frequently, consistently, and at scale. - -### Manual Testing vs. Automated Testing - -Manual testing involves testers executing test cases step-by-step and verifying the results. It is valuable for tasks requiring human intuition, such as exploratory testing or evaluating user interfaces. However, these manual tests tend to be slow, labor-intensive, and inconsistent (we’re all human after all). - -Automated testing, on the other hand, uses scripts and tools to run tests automatically. This type of testing actually includes multiple different types of tests with unit tests being one of the most common ones (although there are alternatives, such as integration tests, e2e tests, etc). This approach offers several benefits: - -* **Speed**: Automated tests execute much faster than manual ones, after all, as long as the system can handle the input, your test will work. -* **Reliability**: Automated tests provide consistent results, eliminating human error. -* **Scalability**: Large test suites can be run efficiently, even across complex scenarios. -* **Reusability**: Test scripts can be reused across multiple projects and cycles. - -All of this has the potential to directly translate into faster feedback loops, higher test coverage, and more reliable software releases. - -### Key Components of DevOps Test Automation - -1. **Continuous Integration (CI):** Continuous Integration is a [DevOps](https://roadmap.sh/devops) practice where developers frequently merge their code changes into a shared repository. With each integration, an automated build and test process is triggered. The goals of CI include: - * Catching issues early by testing incremental code changes. - * Ensuring that new changes don’t break the existing codebase. - * Reducing integration problems by addressing conflicts early and often. -2. **Continuous Delivery (CD):** Continuous Delivery ensures that the codebase is always in a deployable state. It extends CI by automating the deployment process to staging or production environments. The key principles of CD include: - * **Automated Deployment Pipelines**: Using tools like Azure DevOps, AWS CodePipeline, or CircleCI (to name a few) to deploy code with minimal manual intervention. - * **Environment Consistency**: Ensuring that deployments work reliably across staging, testing, and production environments. - * **Fast and Reliable Releases**: Enabling teams to ship new features and fixes to customers quickly and confidently. -3. **Continuous Testing:** Continuous Testing integrates automated testing throughout the development lifecycle, from development to deployment. It is designed to provide immediate feedback on the quality and risks of a release, ensuring that issues are detected and resolved early. - Key aspects of Continuous Testing include: - * **Test Coverage**: Automated tests cover unit, integration, API, performance, and end-to-end scenarios to ensure the entire application is tested. - * **Early Risk Detection**: Running tests early in the pipeline to identify potential business risks associated with the release. - * **Rapid Feedback**: Providing developers with actionable insights within minutes of a code change. - -### Why DevOps Test Automation Matters - -By combining CI, CD, and Continuous Testing into a cohesive DevOps strategy, organizations can accelerate delivery while maintaining high software quality. Test automation reduces the manual effort required for testing, provides faster feedback, and ensures that every code change is reliable and ready for production. - -## Challenges in DevOps Test Automation - -While DevOps test automation brings significant benefits, implementing and maintaining it comes with its own set of challenges. These problems usually come from cultural, technical, and operational complexities. - -Here’s a closer look at some common challenges and how they impact DevOps teams: - -### Cultural and Organizational Barriers - -One of the biggest obstacles to successful DevOps test automation is resistance to change within the organization. DevOps relies on collaboration between development, testing, and operations teams, but traditional silos can make this difficult. - -For example, development and operations teams may have conflicting goals, such as speed versus stability. There might be a lack of shared ownership for testing that can lead to gaps in responsibility, effectively not having anyone owning the practice. - -And finally, teams may resist adopting new tools or affecting the existing (and working) development lifecycle due to unfamiliarity or fear of disrupting established workflows. - -#### Strategies to Overcome - -Consider fostering a collaborative culture by promoting shared goals and open communication. Make sure to provide training and resources to help teams adapt to new tools and methodologies, this will lower the entry barrier and show how new tools and practices actually can improve existing processes. - -In the end, encourage leadership to advocate for DevOps practices, emphasizing their long-term value to the organization. - -### Tool Integration and Maintenance - -DevOps pipelines often rely on a diverse set of tools for version control, CI/CD, test automation, and deployment. Integrating these tools into a cohesive workflow can be difficult, especially when dealing with legacy systems or custom applications. - -If you’re not careful during the tool selection process, or if you just don’t have control over it, you might end up in a situation where these tools may not seamlessly integrate with each other, leading to broken pipelines or data silos. - -On the other hand, even if they do work well together, maintaining automated test scripts can be time-consuming, especially as applications evolve. - -And finally, frequent updates to tools or frameworks can potentially introduce compatibility issues. - -#### Strategies to Overcome - -If you have the ability to pick or change existing solutions, make sure to use platforms like Jenkins, Azure DevOps, or GitLab that support a wide range of integrations. - -Try to adopt modular test automation frameworks to simplify script maintenance. Remember to handle this code like you would the code of any large software project. - -And while you’re at it, if you want to avoid the build-up of technical debt in your toolchain, try to regularly review and update toolchains to ensure compatibility. - -### Scalability and Performance Issues - -As applications grow in complexity and user bases expand, test automation frameworks must scale to handle increased workloads. However, ensuring scalability while maintaining performance is not a trivial task. - -These frameworks will struggle to handle large test suites or datasets, causing either tests to run slow, or even crash for reasons completely unrelated to the code they’re testing. If you’re not careful, performance bottlenecks can occur when running tests on shared resources potentially affecting the main system through your tests. - -#### Strategies to Overcome - -At this stage, you have to be smart about how you implement testing. Consider using parallel test execution to reduce overall test runtime. - -Use cloud-based testing platforms like Sauce Labs or BrowserStack to scale testing infrastructure dynamically and much faster for a fraction of the price you’d have to pay if you were to do it yourself. - -Finally, optimize test suites by removing redundant or outdated tests and prioritizing high-impact cases. In other words, consider that some types of tests might be better off applied to only a portion of your app (key features) instead of testing the entire system. - -### Choosing the Right Test Automation Tool - -Test automation tools form the backbone of successful automation testing strategies. The right tools can simplify workflows, improve test coverage, and ensure seamless integration with your CI/CD pipeline. - -Now the question is: how do you pick the right automation tool for you? - -#### Key considerations when choosing tools - -Not every tool is going to be good for you, either because of technical incompatibilities, skill issues from your team, or just budget constraints. So make sure to apply the following 5 principles when deciding on the best automation tools for your project: - -1. **Compatibility with Your Tech Stack**: Ensure the tool supports the programming languages, frameworks, and platforms your team uses. For example, if you develop web applications in JavaScript, tools like Cypress or Playwright may be a good fit. -2. **Integration with CI/CD Pipelines**: Tools should integrate smoothly with CI/CD platforms like Jenkins, GitLab, Azure DevOps, or CircleCI to enable automated testing as part of your pipeline. -3. **Ease of Use and Learning Curve**: Select tools that align with your team’s expertise or offer a low learning curve. Open-source tools with robust documentation and community support can accelerate adoption. -4. **Scalability and Performance**: Tools must handle increasing complexity and volume as your test suites and applications grow. -5. **Cost and Licensing**: Assess the budget and licensing model. Many open-source tools offer powerful capabilities without added costs, but commercial tools may provide enhanced features or enterprise support. - -#### Popular Options - -While the following is not an exhaustive list, it’s a great starting point if you’re looking for the right tools and you can’t decide where to start. - -1. **Web Application Testing**: - * **Selenium**: A widely-used open-source tool for browser automation. It supports multiple programming languages and browsers. - * **Cypress**: Known for its fast execution and user-friendly interface, Cypress is ideal for modern web applications. - * **Playwright**: Developed by Microsoft, Playwright excels at end-to-end testing across multiple browsers and platforms. -2. **API Testing**: - * **Postman**: Offers a simple interface for creating and executing API tests, with support for automation. - * **RestAssured**: A Java-based tool for testing RESTful web services, integrated easily into Java projects. - * **SoapUI**: Useful for testing both REST and SOAP APIs, with enterprise-level capabilities. -3. **Mobile Testing**: - * **Appium**: Open-source and versatile, Appium supports testing for native, hybrid, and mobile web apps across iOS and Android. - * **XCUITest/Espresso**: Native tools for iOS and Android testing, offering deep integration with the respective ecosystems. -4. **Performance Testing**: - * **JMeter**: Open-source and widely used for load and performance testing of applications, particularly web and API testing. - * **Gatling**: A tool focused on high-performance and scalability testing, ideal for developers. -5. **Test Management and Reporting**: - * **TestRail**: A comprehensive tool for test case management, tracking, and reporting. - * **Allure Report**: An open-source reporting tool that integrates with test automation frameworks to provide detailed insights. - -## Best Practices for Effective DevOps Test Automation - -Successfully implementing DevOps test automation requires addressing challenges with proactive strategies and best practices. These practices help teams build a strong foundation for collaboration, skill development, and process improvement. Let’s take a closer look at the key actions organizations can take: - -### Foster a collaborative culture - -DevOps thrives on the seamless collaboration between development, testing, and operations teams. In fact, it depends on it. A lack of alignment can slow down progress and reduce the effectiveness of test automation. - -We can encourage that collaboration by promoting cross-functional teams (as opposed to vertical teams working in isolation from each other with their own individual priorities) where responsibilities are shared, and everyone is invested in testing and quality. - -Fostering that sense of belonging to a greater group by holding regular meetings or retrospectives to discuss challenges, share insights, and align goals. - -Finally, using collaborative tools like Jira, Slack, or Confluence to ensure transparent communication and knowledge sharing. - -### Invest in Training and Skill Development - -Test automation tools and frameworks often require specialized knowledge, and a lack of expertise can hinder adoption. Ensuring team members have the necessary skills is critical for long-term success. The good news is that skills can be trained and developed over time. So let’s take a look at some ideas to grow them. - -The most obvious one is to conduct workshops and training sessions on test automation tools like Selenium, Cypress, or Appium. That way you update your teams about the latest trends in the industry while at the same time, providing them with practical knowledge they can then, turn around, and apply to their own projects. - -Certifications are also a fantastic way to skill up from the “official” sources. Who better to teach your team how to use a product than those who can certify you on it? So make sure to encourage team members to obtain certifications in DevOps practices and tools. - -Finally, you can also provide access to online learning platforms such as Coursera, Pluralsight, or Udemy for self-paced learning. - -Investing in skill development empowers teams to use automation tools effectively, leading to higher productivity and better results. - -### Select Appropriate Tools and Frameworks - -The success of test automation depends heavily on choosing the right tools and frameworks. Tools that align with your technology stack and organizational needs will simplify implementation and reduce maintenance overhead. - -So make sure to assess the compatibility of tools with your existing technology stack (e.g., programming languages, CI/CD systems). Prioritize tools with strong community support and regular updates, such as Jenkins, Selenium, or TestNG. - -Opt for tools that offer integrations with your DevOps pipeline to streamline workflows, the more out-of-the-box integrations you can get, the better. - -### Monitor and Optimize Test Automation Processes - -Contrary to popular belief, implementing test automation is not a one-time task. Continuous monitoring and improvement are necessary to ensure that your processes remain efficient and effective as your application and pipeline evolve. In other words, stay on top of your testing process, pick up on problems as soon as they show up on your radar and implement some of the following best practices. - -First things first, make sure to use metrics like test coverage, execution time, and defect detection rates to evaluate effectiveness. Compare those values release after release to understand the evolution of your project’s organization. - -If monitoring is what you’re looking for, then consider implementing dashboards for real-time visibility into test automation performance. - -Regularly review test cases to eliminate redundant or outdated ones, focusing on high-priority scenarios. - -And of course, try to gather feedback from developers and testers to identify pain points and opportunities for improvement. - -## Conclusion - -DevOps test automation is a cornerstone of modern software development, enabling organizations to accelerate software delivery without compromising quality. By integrating automated testing into the development lifecycle, teams can identify and resolve issues early. - -To fully realize its benefits, organizations must address challenges such as cultural barriers, tool integration, and scalability. By fostering collaboration, investing in training, choosing the right tools and keeping a close eye on your processes, you’ll start seeing how those KPIs start moving over time, showing a clear improvement over your current operations speed. - diff --git a/src/data/guides/devops-tools.md b/src/data/guides/devops-tools.md deleted file mode 100644 index 1c0be420b..000000000 --- a/src/data/guides/devops-tools.md +++ /dev/null @@ -1,419 +0,0 @@ ---- -title: '21 Best DevOps Tools from Essentials to Advanced and Pro' -description: 'Maximize efficiency with top DevOps tools for all expertise levels. Find the perfect solutions to automate, integrate, and optimize your software delivery pipeline.' -authorId: fernando -excludedBySlug: '/devops/tools' -seo: - title: '21 Best DevOps Tools from Essentials to Advanced and Pro' - description: 'Maximize efficiency with top DevOps tools for all expertise levels. Find the perfect solutions to automate, integrate, and optimize your software delivery pipeline.' - ogImageUrl: 'https://assets.roadmap.sh/guest/best-devops-tools-3vo92.jpg' -relatedGuidesTitle: 'Other Guides' -relatedGuidesId: devops -isNew: false -type: 'textual' -date: 2024-11-29 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![The best DevOps tools to use](https://assets.roadmap.sh/guest/best-devops-tools-3vo92.jpg) - -Choosing the right DevOps tools can make a significant difference in how effectively your team develops and delivers software. With so many options available in 2025, it can be challenging to determine which tools best fit your organization’s needs. - -This article is designed to help development and operations teams (and their organizations) navigate the landscape of DevOps tools, from the most essential to advanced and pro-level solutions. - -Whether you are just starting with [DevOps](https://roadmap.sh/devops) or looking to enhance your existing processes, this guide will provide clear and practical information to support your decision-making. - -Throughout this article, we’ll cover and evaluate the following technologies: - -* **Continuous Integration/Continuous Deployment (CI/CD) tools:** Jenkins, GitLab, CircleCI, GitHub Actions -* **Infrastructure as Code (IaC) Tools:** Terraform, Pulumi -* **Containerization and Orchestration Tools:** Docker, Kubernetes, Docker Swarm -* **Logging and Monitoring Tools:** Prometheus, ELK, Grafana, Loki -* **Configuration Management Tools:** Chef, Puppet, Ansible -* **Secrets Management**: HashiCorp Vault, Sealed Secrets, SOPS -* **Security and Compliance Tools:** OWASP ZAP, Aqua Security. - -## Evaluation Criteria for DevOps Tools - -When selecting the best DevOps tools for your organization, it's important to evaluate each option based on several key factors. These criteria will help ensure that the DevOps tools you choose not only meet your current needs but also support your growth and adapt to future changes. Below are the primary categories we use to assess each DevOps tool: - -* **Performance:** Efficiency, speed, and resource utilization to enhance productivity. -* **Scalability:** Ability to handle growing workloads and infrastructure seamlessly. -* **Community and Ecosystem:** Availability of plugins, integrations, and strong community support. -* **Learning Curve:** Ease of adoption and quality of documentation for quick onboarding. - - -## Continuous Integration/Continuous Deployment (CI/CD) tools - -![CI/CD Tools](https://assets.roadmap.sh/guest/contiuous-integration-vs-continous-deploment-9c872.png) - -This specific type of DevOps tools are essential for automating the [software development lifecycle](https://roadmap.sh/devops/lifecycle) as well as the deployment process. A continuous integration & continuous deployment platform (which takes continuous delivery one step further) enables developers to integrate code changes continuously (thus the name\!) and deploy them efficiently. Below, we evaluate some of the top integration & deployment DevOps tools in this category available in 2025 based on key criteria, each rated out of five stars. - -### **GitHub Actions** - -* **Performance** ★★★★★ - GitHub Actions offers efficient execution of CI/CD workflows by leveraging GitHub’s robust infrastructure. This seamless integration ensures that workflows run smoothly and reliably, minimizing delays and improving overall performance. The ability to trigger actions directly from GitHub repositories enhances the speed and efficiency of the development and deployment processes. -* **Scalability** ★★★★★ - GitHub Actions scales exceptionally well with GitHub’s infrastructure, making it suitable for projects of all sizes, from small personal repositories to large enterprise applications. The platform can handle high volumes of workflows and concurrent executions, ensuring that your CI/CD pipelines remain effective as your project and team grow. -* **Community and Ecosystem** ★★★★★ - GitHub Actions benefits from an extensive marketplace filled with numerous pre-built actions that can be easily integrated into your workflows. This rich ecosystem allows users to find and implement actions for a wide variety of tasks, from testing and deployment to notifications and security checks. The strong community support further enhances the value of GitHub Actions, providing access to shared knowledge and resources. -* **Learning Curve** ★★★★☆ - GitHub Actions is relatively easy to learn, especially for users already familiar with GitHub’s interface and repository management. The use of YAML-based workflows is intuitive and straightforward, and there is ample documentation available to guide users through the setup and configuration process. While it is user-friendly, mastering more advanced features may require additional learning and experimentation. - -### **GitLab** - -* **Performance** ★★★★★ - GitLab CI/CD offers efficient pipeline execution, particularly with its built-in Kubernetes integration. This integration allows for seamless deployment and scaling of applications, enhancing overall performance. GitLab’s optimized runners and caching mechanisms ensure that build and deployment processes are swift and reliable, minimizing downtime and accelerating the development cycle. -* **Scalability** ★★★★★ - GitLab is designed to scale effortlessly with large projects and teams. Its architecture supports the management of multiple pipelines and extensive workloads without sacrificing performance. Whether you’re handling a small repository or a complex, multi-project environment, GitLab can accommodate your needs, making it an excellent choice for growing organizations. -* **Community and Ecosystem** ★★★★☆ - GitLab CI/CD benefits from a strong community and is tightly integrated with GitLab’s comprehensive suite of tools. This integration provides a unified platform for version control, issue tracking, and CI/CD, enhancing productivity and collaboration. While the ecosystem may not be as extensive as Jenkins’s, it still offers a robust set of plugins and integrations that cover most DevOps requirements. -* **Learning Curve** ★★★★☆ - For teams already using GitLab, the learning curve for GitLab CI/CD is relatively gentle. The tool is designed with user-friendly interfaces and comprehensive documentation, making it easier for users to get started. Additionally, GitLab provides extensive tutorials and support resources, which help reduce the time and effort required for onboarding and mastering the tool. - -### **CircleCI** - -* **Performance** ★★★★★ - CircleCI is known for its fast build times, which are achieved through optimized caching and parallelism. These features significantly improve the efficiency of CI/CD pipelines by reducing the time it takes to execute builds and tests. CircleCI’s ability to handle multiple jobs simultaneously ensures that development cycles remain swift and productive, allowing teams to deliver updates quickly. -* **Scalability** ★★★★★ - CircleCI leverages cloud-based infrastructure to scale effortlessly with your project’s needs. Whether you’re handling small projects or large, complex applications, CircleCI can accommodate the increasing demands without any performance degradation. This scalability ensures that as your organization grows, CircleCI remains a reliable and effective CI/CD solution. -* **Community and Ecosystem** ★★★★☆ - CircleCI has a growing ecosystem that offers strong support for modern languages and frameworks. While its ecosystem is not as extensive as some older tools, it is continuously expanding with new integrations and plugins. The active community provides valuable resources, including forums, documentation, and best practices, which help users maximize the tool’s potential. -* **Learning Curve** ★★★★★ - CircleCI is praised for its user-friendly interface and easy setup process. New users can quickly get started with minimal configuration, thanks to clear documentation and intuitive design. This ease of use reduces the time and effort required for onboarding, allowing teams to become productive with CircleCI in a shorter timeframe compared to more complex tools. - -### **Jenkins** - -* **Performance** ★★★★☆ - Jenkins is highly customizable, allowing teams to tailor it to their specific needs through a very complete list of plugins. This flexibility ensures that Jenkins can handle a wide range of tasks efficiently. However, the extensive customization can lead to increased resource consumption, which may impact performance, especially for larger projects or when numerous plugins are in use. Proper optimization and resource management are necessary to maintain optimal performance. -* **Scalability** ★★★★★ - Jenkins excels in scalability, particularly with its support for distributed builds and pipelines. Organizations can set up multiple Jenkins agents across different servers to handle large-scale projects and increased workloads. This distributed architecture ensures that Jenkins can manage growing infrastructure needs without compromising performance (ideally), making it suitable for both small teams and large enterprises. -* **Community and Ecosystem** ★★★★★ - Jenkins boasts the largest CI/CD ecosystem, supported by a vast library of plugins and a strong, active community. This extensive ecosystem allows users to extend Jenkins’s functionality to meet diverse requirements, from integrating with various version control systems to deploying applications across different environments. The active community also means that users have access to a wealth of resources, including tutorials, forums, and regular updates. -* **Learning Curve** ★★★★☆ - Jenkins has a moderate learning curve. While it offers comprehensive documentation and a wealth of tutorials, the initial setup and configuration can be complex. However, once users become familiar with Jenkins’s interface and capabilities, they can leverage its powerful features effectively. The availability of numerous plugins and customization options may require additional time to master. - ---- - -## Infrastructure as Code (IaC) Tools - -![Infrastructure as Code](https://assets.roadmap.sh/guest/infrastructure-as-code-zrjvp.png) - -Infrastructure as Code (IaC) tools are pivotal for automating the provisioning and management of infrastructure, ensuring consistency, scalability, and efficiency in deploying resources. Below, we evaluate some of the leading IaC tools available in 2025 based on key criteria, each rated out of five stars. - -### **Terraform** - -* **Performance** ★★★★★ - Terraform excels in efficient state management and resource provisioning. It uses a declarative language to define infrastructure, which allows for predictable and repeatable deployments. Terraform's ability to manage dependencies and its parallel execution capabilities contribute to its high performance, making infrastructure changes swift and reliable. -* **Scalability** ★★★★★ - Terraform is highly scalable and capable of managing complex and large-scale infrastructures across multiple cloud providers. Its modular approach allows for the reuse of configurations, making it easier to handle expanding infrastructure needs without significant overhead. Whether you're managing a few resources or thousands, Terraform scales seamlessly to meet your demands. -* **Community and Ecosystem** ★★★★★ - Terraform boasts an extensive provider ecosystem, supporting a wide range of cloud platforms, services, and third-party tools. The strong community support means there are plenty of modules, plugins, and extensions available, which can accelerate the setup and customization of your infrastructure. The active Terraform community also provides valuable resources, tutorials, and forums for troubleshooting and best practices. -* **Learning Curve** ★★★★☆ - Terraform has a moderate learning curve, primarily due to its use of the HashiCorp Configuration Language (HCL). While HCL is designed to be human-readable and straightforward, new users may need some time to become proficient. Comprehensive documentation and a wealth of tutorials help mitigate the learning challenges, allowing users to gradually build their expertise. - -### **Pulumi** - -* **Performance** ★★★★☆ - Pulumi utilizes modern programming languages such as JavaScript, TypeScript, Python, Go, and C\#, which can enhance performance by leveraging familiar development practices. This approach allows for more dynamic and powerful infrastructure definitions. However, performance can vary depending on the complexity of the code and the efficiency of the underlying infrastructure providers. -* **Scalability** ★★★★★ - Pulumi scales well with cloud-native applications and microservices architectures. Its support for infrastructure modularity and reusable components allows organizations to manage complex and distributed systems effectively. Pulumi's ability to integrate with various cloud providers and services ensures that it can handle growing infrastructure demands seamlessly. -* **Community and Ecosystem** ★★★★☆ - Pulumi has an emerging ecosystem that is steadily growing in support of multiple programming languages and cloud platforms. While it may not yet match the extensive ecosystems of more established tools like Terraform or Ansible, Pulumi's active development and increasing adoption are rapidly expanding its community and available resources. -* **Learning Curve** ★★★★☆ - Pulumi has a higher learning curve for users who are not familiar with the languages it supports. While leveraging general-purpose languages offers flexibility and power, it requires users to have coding expertise. Comprehensive documentation and examples help mitigate this challenge, but users may need additional time to become proficient compared to tools with simpler configuration languages. - -## Containerization and Orchestration Tools - -![Container Orchestration](https://assets.roadmap.sh/guest/container-orchestration-i0rgu.png) - -### Docker - -* **Performance** ★★★★★ - Docker excels at containerization with minimal overhead, offering fast and efficient deployment of applications. Its lightweight architecture ensures high performance while isolating environments effectively. -* **Scalability** ★★★★☆ - Docker scales efficiently for both small projects and large deployments. However integration with orchestration tools like Kubernetes is often necessary to manage complex systems at scale. -* **Community and Ecosystem** ★★★★★ - As the largest container ecosystem, Docker boasts extensive image repositories, plugins, and integrations. Its active community provides robust support and a wealth of learning resources. -* **Learning Curve** ★★★★☆ - Docker is moderately easy to learn, especially for developers familiar with virtualization or container concepts. Its comprehensive documentation and tutorials make it accessible, though advanced features require more exploration. - -### Kubernetes - -* **Performance ★★★★★** - Kubernetes is a powerhouse in container orchestration, delivering highly efficient performance for containerized applications. Its advanced features like auto-scaling, load balancing, and self-healing ensure optimal resource utilization and application uptime. -* **Scalability ★★★★★** - Kubernetes is built for exceptional scalability, supporting distributed, large-scale systems with thousands of containers across multi-cloud or hybrid environments. Its design enables seamless scaling of both applications and infrastructure. -* **Community and Ecosystem ★★★★★** - With one of the largest and most vibrant ecosystems, Kubernetes offers a wealth of tools, extensions, and integrations. The active community provides extensive support, including tutorials, forums, and open-source contributions, making it an industry standard for container orchestration. -* **Learning Curve ★★☆☆☆** - Kubernetes has a steep learning curve due to its complex architecture and vast feature set. While extensive documentation and resources are available, mastering Kubernetes requires significant time, effort, and hands-on experience, especially for managing advanced deployments. - -### Docker Swarm - -* **Performance** ★★★★☆ - Docker Swarm delivers efficient container orchestration with lower overhead than more complex systems like Kubernetes. Its simplified approach ensures quick deployment and management of containerized applications, making it a great choice for lightweight orchestration tasks. -* **Scalability** ★★★☆☆ - Docker Swarm is ideal for small to medium-scale deployments. While it can handle moderate workloads, it lacks the advanced scalability features required for managing large systems distributed across multiple environments. -* **Community and Ecosystem** ★★★☆☆ - Docker Swarm integrates seamlessly with Docker, leveraging Docker's extensive ecosystem. However, its dedicated ecosystem is smaller and less active compared to Kubernetes, limiting access to third-party tools and advanced community-driven solutions. -* **Learning Curve** ★★★★☆ - Docker Swarm is straightforward and easy to learn, especially for developers already familiar with Docker. Its intuitive setup and configuration make it an excellent starting point for teams looking to adopt container orchestration without diving into the complexities of Kubernetes. - -## Logging and Monitoring Tools - -![Monitoring and Logging](https://assets.roadmap.sh/guest/monitoring-and-logging-2quwy.png) - -### Prometheus - -* **Performance** ★★★★★ - Prometheus is highly efficient at collecting, storing, and querying time-series metrics. Its pull-based architecture and optimized storage engine make it ideal for monitoring high-performance systems. -* **Scalability** ★★★★★ - Prometheus scales well for dynamic environments, particularly in microservices and container-based architectures. With federation and sharding, it can handle large-scale setups effectively. -* **Community and Ecosystem** ★★★★★ - Prometheus has a robust ecosystem, with tools like Grafana for visualization and Alertmanager for managing alerts. Its strong community provides a wealth of tutorials, plugins, and integrations, making it a staple in monitoring stacks. -* **Learning Curve** ★★★☆☆ - Prometheus is moderately challenging to learn, mainly due to its PromQL query language. While its documentation is comprehensive, understanding and leveraging PromQL effectively requires some learning effort. - -### ELK Stack (Elasticsearch, Logstash, Kibana) - -* **Performance** ★★★★★ - The ELK Stack excels in log aggregation, storage, and search. Elasticsearch provides fast, scalable search capabilities, while Logstash processes and transforms logs efficiently. -* **Scalability** ★★★★★ - ELK is highly scalable, capable of handling vast volumes of logs and data across distributed architectures. It is widely used in large-scale environments requiring centralized logging solutions. -* **Community and Ecosystem** ★★★★★ - With an extensive ecosystem, the ELK Stack supports numerous plugins, integrations, and use cases. The community is highly active, offering ample resources, forums, and shared configurations. -* **Learning Curve** ★★☆☆☆ - The learning curve for ELK is steep, as users must understand and configure each component—Elasticsearch, Logstash, and Kibana—effectively. The complexity increases when scaling and optimizing performance. - -### Loki - -* **Performance** ★★★★☆ - Loki excels at aggregating and querying logs efficiently, particularly in Kubernetes and microservices setups. However, it doesn't index logs by content, relying on labels instead, which can slightly limit complex querying performance compared to fully indexed systems. -* **Scalability** ★★★★★ - Loki scales seamlessly in distributed environments. Its architecture, mirroring Prometheus, makes it highly suitable for large-scale deployments. With the ability to shard and replicate data, Loki effectively supports dynamic and expansive infrastructures. -* **Community and Ecosystem** ★★★★★ - Loki benefits from being part of the Grafana ecosystem, providing tight integration with Grafana dashboards for unified observability alongside Prometheus. Its active community and extensive plugins enhance its usability, ensuring strong support and continuous improvements. -* **Learning Curve** ★★★☆☆ - Loki is straightforward to set up and use, especially for those familiar with Prometheus and Grafana. However, the reliance on labels for querying can require adjustment for users accustomed to full-text search in other logging systems. - -### Grafana - -* **Performance** ★★★★★ - Grafana is a high-performance tool for data visualization, providing real-time, interactive dashboards for monitoring and analytics. Its ability to integrate with multiple data sources ensures seamless performance across diverse environments. -* **Scalability** ★★★★★ - Grafana scales effortlessly, supporting large datasets and multiple data sources. It is well-suited for both small and enterprise-level monitoring setups. -* **Community and Ecosystem** ★★★★★ - With a strong community and a wealth of plugins and integrations, Grafana is a leader in the visualization space. Its ecosystem supports various data sources like Prometheus, InfluxDB, and Elasticsearch, among others. -* **Learning Curve** ★★★★☆ - Grafana is relatively easy to learn, with a user-friendly interface and intuitive dashboard creation. Beginners can get started quickly, while advanced users can leverage its extensive customization options. - -## Configuration Management Tools - -![Configuration Management Tools](https://assets.roadmap.sh/guest/configuration-management-tools-fjrt5.png) - -### Chef - -* **Performance** ★★★★☆ - Chef is highly efficient for managing complex configurations in diverse environments. Its Ruby-based approach allows for flexible and powerful automation, though it can be resource-intensive in large-scale setups. -* **Scalability** ★★★★★ - Chef scales well for managing large infrastructures, making it ideal for enterprise-level deployments. Its ability to handle complex dependencies ensures smooth operations in extensive setups. -* **Community and Ecosystem** ★★★★★ - Chef has a mature ecosystem with a wide range of pre-built cookbooks and strong community support. This provides ample resources, from tutorials to shared configurations, enabling faster implementation. -* **Learning Curve** ★★☆☆☆ - Chef has a steep learning curve, as it requires knowledge of Ruby and Chef’s DSL. While its documentation is comprehensive, mastering its features and effectively writing custom cookbooks can take significant effort. - -### Puppet - -* **Performance** ★★★★★ - Puppet delivers robust performance, especially in large-scale deployments. Its agent-based model ensures reliable configuration management and state enforcement across environments. -* **Scalability** ★★★★★ - Puppet is highly scalable and built to handle enterprise-grade infrastructures with thousands of nodes. Its declarative approach simplifies management in large, distributed systems. -* **Community and Ecosystem** ★★★★★ - Puppet boasts an extensive ecosystem with a rich collection of pre-built modules. Its strong community offers excellent support, resources, and integrations for diverse use cases. -* **Learning Curve** ★★☆☆☆ - The learning curve for Puppet is steep due to its custom DSL and complex architecture. Understanding its declarative syntax and implementing advanced configurations requires considerable time and expertise. - -### Ansible - -* **Performance** ★★★★★ - Ansible's agentless architecture provides fast execution and minimal overhead, making it an efficient choice for managing configurations and automating workflows. -* **Scalability** ★★★★★ - Ansible scales effectively, managing large infrastructures with ease. Its simple architecture and modular design allow for efficient scaling across environments. -* **Community and Ecosystem** ★★★★★ - Ansible has a strong community and a vast collection of modules, known as Ansible Galaxy. Its active ecosystem ensures users can find support, share playbooks, and integrate with various tools seamlessly. -* **Learning Curve** ★★★★☆ - Ansible is relatively easy to learn, thanks to its straightforward YAML-based playbooks. Beginners can quickly get started, while advanced users can explore its powerful automation features with minimal learning overhead. - -## Secrets Management - -### HashiCorp Vault - -* **Performance** ★★★★★ - HashiCorp Vault excels at secret management and encryption, offering high performance and reliability for securely storing and accessing sensitive information. Its ability to handle dynamic secrets and encryption services ensures efficient operations in diverse environments. -* **Scalability** ★★★★★ - Vault scales seamlessly, making it suitable for managing secrets across large and distributed infrastructures. It integrates well with various platforms and services, providing robust support for enterprise-level setups. -* **Community and Ecosystem** ★★★★★ - Vault has a strong ecosystem, with numerous integrations and a dedicated community providing extensive support. Its documentation, tutorials, and plugins make it highly adaptable to various use cases. -* **Learning Curve** ★★★☆☆ - The learning curve is moderate, requiring users to understand Vault's architecture, policies, and authentication methods. While its documentation is thorough, setting up and managing complex configurations demands time and expertise. - -### **SOPS (Simple and Secure Operations)** - -* **Performance** ★★★★★ - SOPS is highly efficient for encrypting and decrypting files. It integrates seamlessly with various cloud KMS solutions and supports multiple encryption formats (YAML, JSON, ENV, etc.), making it a robust tool for managing sensitive data. -* **Scalability** ★★★★☆ - SOPS scales well for environments with multiple developers or teams, as it can handle keys for multiple users. However, managing large numbers of encrypted files might require careful orchestration and automation to avoid complexity. -* **Community and Ecosystem** ★★★★★ - SOPS has a strong community and integrates well with popular infrastructure-as-code tools like Terraform, Kubernetes, and Helm. Its compatibility with multiple key management systems (AWS KMS, GCP KMS, Azure Key Vault, PGP, etc.) makes it highly versatile and widely adopted. -* **Learning Curve** ★★★☆☆ - SOPS is relatively straightforward to use for basic encryption needs, but understanding the nuances of key management and integrating it into CI/CD pipelines requires some learning and experimentation. - -### **Sealed Secrets (Kubernetes)** - -* **Performance** ★★★★★ - Sealed Secrets is purpose-built for Kubernetes and performs exceptionally well in encrypting and decrypting secrets. Its server-side controller efficiently handles encryption, ensuring high availability and low latency in managing secrets. -* **Scalability** ★★★★★ - Designed for Kubernetes, Sealed Secrets scales seamlessly with clusters of any size. Its encryption model allows secrets to be safely shared and stored in version control without compromising security, making it ideal for large, distributed teams. -* **Community and Ecosystem** ★★★★☆ - Sealed Secrets has a dedicated but smaller community compared to broader tools like SOPS. However, its Kubernetes-specific nature limits its ecosystem integrations outside Kubernetes workflows. It works well with Helm and GitOps practices but is less versatile for non-Kubernetes use cases. -* **Learning Curve** ★★★☆☆ - Sealed Secrets is easy to adopt for Kubernetes users due to its intuitive interface and integration with the ecosystem. However, understanding the controller mechanics and properly managing the key rotation process might require some Kubernetes expertise. - -## Security and Compliance Tools - -![Security and Compliance Tools](https://assets.roadmap.sh/guest/security-compliance-vpz26.png) - -Tools in this category empower security teams to identify vulnerabilities, manage secrets, enforce compliance policies, and maintain secure environments. By integrating these tools early in the development lifecycle, organizations can shift security left and build robust systems without compromising speed or agility. - -### OWASP ZAP - -* **Performance ★★★★★** - OWASP ZAP is highly efficient for automated security testing and vulnerability scanning, providing reliable results for web application security. Its flexibility makes it a valuable tool for identifying vulnerabilities in various environments. -* **Scalability ★★★★☆** - ZAP is suitable for both small-scale projects and larger applications, scaling effectively with the needs of diverse teams. However, for extensive enterprise environments, it may require additional tuning and integration. -* **Community and Ecosystem ★★★★★** - With strong community support, OWASP ZAP offers numerous plugins, extensions, and integrations. Its active user base ensures a wealth of resources, from shared scripts to tutorials and forums. -* **Learning Curve ★★★★☆** - ZAP is relatively easy to learn, especially for users familiar with security testing concepts. Its intuitive interface and extensive resources allow beginners to get started quickly while offering advanced features for seasoned testers. - -### Aqua Security - -* **Performance ★★★★★** - Aqua Security provides comprehensive protection for containerized applications and Kubernetes, ensuring efficient detection and mitigation of vulnerabilities. Its advanced features, such as runtime protection and compliance checks, enhance performance across cloud-native environments. -* **Scalability ★★★★★** - Aqua Security scales efficiently with modern DevOps and cloud-native environments, making it ideal for enterprises with dynamic and distributed infrastructures. -* **Community and Ecosystem ★★★★☆** - Aqua Security has a growing ecosystem with strong support for modern [DevOps practices](https://roadmap.sh/devops/best-practices). While its community is not as large as some established tools, its partnerships and integrations ensure robust adaptability. -* **Learning Curve ★★★☆☆** - The learning curve for Aqua Security is moderate, requiring users to understand container security principles and Kubernetes configurations. Its clear documentation and training resources ease the process, though advanced features may take time to master. - -## Expert Recommendations on DevOps Tools - -![Expert Recommendations on DevOps Tools](https://assets.roadmap.sh/guest/expert-advice-best-devops-tools-qyihk.png) - -If you’re wondering after all of this, which are the best options for you in terms of DevOps tools, then you’re not alone. The problem is that you can’t really decide without some sort of context around the options. - -So using the following summarized table, I’m going to try and help you decide on the right DevOps tools for your needs. - -![DevOps tools summary](https://assets.roadmap.sh/guest/devops-tools-summary.png) - -### Best CI/CD Tool: GitLab CI/CD - -#### Ratings for GitLab CI/CD - -* **Performance: ★★★★★** -* **Scalability: ★★★★★** -* **Community and Ecosystem: ★★★★★** -* **Learning Curve: ★★★★☆** - -**Why:** -GitLab is the top choice for CI/CD because it provides an all-in-one solution for version control, continuous deployment & integration pipelines, and deployment. It supports complex workflows, offers strong scalability for enterprise needs, and has extensive community and ecosystem support. Its YAML-based pipeline definitions make it powerful yet user-friendly. Compared to Jenkins, it requires less setup and maintenance, and its integration with GitLab repositories makes it an ideal choice for teams already using GitLab. - -### Best Infrastructure as Code Tool: Terraform \+ HashiCorp Vault - -#### Ratings for HashiCorp Vault - -* **Performance: ★★★★★** -* **Scalability: ★★★★★** -* **Community and Ecosystem: ★★★★★** -* **Learning Curve: ★★★☆☆** - -#### Ratings for Terraform - -* **Performance: ★★★★★** -* **Scalability: ★★★★★** -* **Community and Ecosystem: ★★★★★** -* **Learning Curve: ★★★★☆** - -**Why:** -While Terraform leads in IaC, HashiCorp Vault complements it by managing secrets and sensitive credentials. Vault integrates seamlessly into IaC workflows, ensuring secure and scalable deployments. Its robust ecosystem and advanced security features make it indispensable for IaC practices. - -### Best Containerization Tool: Docker - -#### Ratings for Docker - -* **Performance: ★★★★★** -* **Scalability: ★★★★☆** -* **Community and Ecosystem: ★★★★★** -* **Learning Curve: ★★★★☆** - -**Why:** -Docker is the cornerstone of containerization, offering lightweight, efficient, and reliable application deployment. Its extensive ecosystem of pre-configured images simplifies container management, and it integrates well with orchestration tools like Kubernetes for scalable environments. - -### Best Monitoring and Logging Tool: Prometheus (with Grafana) - -#### Ratings for Prometheus - -* **Performance: ★★★★★** -* **Scalability: ★★★★★** -* **Community and Ecosystem: ★★★★★** -* **Learning Curve: ★★★☆☆** - -#### Ratings for Grafana - -* **Performance: ★★★★★** -* **Scalability: ★★★★★** -* **Community and Ecosystem: ★★★★★** -* **Learning Curve: ★★★★☆** - -**Why:** -Prometheus excels in collecting and storing time-series metrics, while Grafana provides powerful, real-time visualization. Together, they offer an unparalleled monitoring and logging solution for cloud-native environments, ensuring scalability and robust performance insights. - -### Best Configuration Management Tool: Puppet - -#### Ratings for Puppet - -* **Performance: ★★★★★** -* **Scalability: ★★★★★** -* **Community and Ecosystem: ★★★★★** -* **Learning Curve: ★★☆☆☆** - -**Why:** -Puppet's agent-based model ensures consistent and reliable configuration management across large-scale deployments. Its extensive module library and declarative approach make it a go-to choice for managing complex infrastructures, despite its steep learning curve. - -### Best Security and Compliance Tool: HashiCorp Vault - -#### Ratings for HashiCorp Vault - -* **Performance: ★★★★★** -* **Scalability: ★★★★★** -* **Community and Ecosystem: ★★★★★** -* **Learning Curve: ★★★☆☆** - -**Why:** -HashiCorp Vault is the gold standard for managing secrets and ensuring encryption. Its scalability, performance, and strong integrations make it ideal for maintaining compliance and securing sensitive data in dynamic, large-scale environments. - -## Conclusion - -Selecting the right DevOps tool requires careful consideration of your organization’s specific needs, project scale, and team expertise. While tools like Kubernetes and ELK Stack excel in scalability, others like Docker Swarm and OWASP ZAP are better suited for simpler setups. - -Aligning what the tool can do with your context (your workflow) ensures maximum efficiency and minimal overhead. Start small, but think big—plan for tools that can grow with your infrastructure. - -For a deeper dive into building a robust DevOps workflow, explore the [**DevOps Roadmap**](https://roadmap.sh/devops). It’s a comprehensive guide to understanding essential [skills](https://roadmap.sh/devops/skills), technologies, and processes for optimizing your development pipeline. \ No newline at end of file diff --git a/src/data/guides/devops-vs-agile.md b/src/data/guides/devops-vs-agile.md deleted file mode 100644 index 6c67ba164..000000000 --- a/src/data/guides/devops-vs-agile.md +++ /dev/null @@ -1,204 +0,0 @@ ---- -title: 'DevOps vs Agile Methodology: Key Differences & Applications' -description: 'Explore the contrasts between DevOps and Agile: Understand their principles, differences, and how to apply them effectively in projects.' -authorId: ekene -excludedBySlug: '/devops/vs-agile' -seo: - title: 'DevOps vs Agile Methodology: Key Differences & Applications' - description: 'Explore the contrasts between DevOps and Agile: Understand their principles, differences, and how to apply them effectively in projects.' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-vs-agile-methodology-tlxj8.jpg' -relatedTitle: 'Other Guides' -relatedGuidesId: 'devops' -isNew: false -type: 'textual' -date: '2025-01-17' -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![DevOps vs Agile Methodology](https://assets.roadmap.sh/guest/devops-vs-agile-methodology-tlxj8.jpg) - -Agile and DevOps are modern approaches organizations use to tackle some of the most complex challenges in software engineering. Whether used to complement each other or as separate methodologies, they can be tricky to define, and the line between the two often appears blurred. - -Both [DevOps](https://roadmap.sh/devops) and Agile are designed to help organizations build robust software. But when should you choose one over the other? Which approach works best for your project? Can you combine them effectively to maximize their benefits? - -Having worked as an engineer on projects of all sizes, I've experienced firsthand how Agile and DevOps shape workflows and outcomes. In this guide, I'll discuss their core principles, key differences, and practical use cases to help you decide which approach fits your needs. - -## DevOps vs. Agile: Core values and goals - -**Agile** is a project management and software development approach that breaks down the project into several dynamic phases, **known as sprints**, rather than completing an entire project at once. This approach enables teams to adapt to changes quickly, continuously improve the delivery pipeline, and stay focused on meeting customer needs. - -It was born from the [Agile Manifesto](https://agilemanifesto.org/) in 2001 as a response to the challenges faced with traditional project management models like the Waterfall method, which often leads to delay, rigidness, and disconnects between customers' demands and what developers have built. - -![Agile timeline](https://assets.roadmap.sh/guest/agile-timeline-174xo.png) - -The core values of Agile methodologies are: - -1. **Individual over processes and tools**: To prioritize human communication and collaboration across cross-functional teams rather than working independently. -2. **Responding to change over following a plan**: To embrace changing requirements at every stage of the development process. The flexibility allows the team to quickly change workflow and strategies without derailing the entire project. -3. **Customer collaboration over contract negotiation**: To incorporate continuous customer feedback and use it to shape the project deliverables and align the outcome. -4. **Working software over comprehensive documentation**: To ensure that the software that Agile teams develop works effectively, while additional tasks like documentation should not take center stage in the development process. - -When you use Agile in your application development or other types of software development process, it offers numerous benefits, such as: - -- Responding quickly to market changes and customer feedback. -- Improving collaboration by fostering open communication, frequent iterations, and shared ownership. -- Increasing customer satisfaction throughout the development process. -- Enhancing quality through frequent reviews and continuous testing. -- Empowering teams to innovate and solve problems creatively. - -**DevOps,** on the other hand, is a set of tools, practices, and a cultural philosophy that bridges the gap between development (Dev) and operations (Ops) to enhance the delivery pipelines. It emphasizes automation, continuous integration/continuous delivery (CI/CD), and monitoring to ensure rapid and reliable software deployment. - -![DevOps timeline](https://assets.roadmap.sh/guest/devops-timeline-f5wbv.png) - -DevOps evolved as an extension of Agile to solve the bottleneck around operations, particularly in deployment, releases, and post-production maintenance. Its core values focus on: - -1. **Collaboration and communication**: Foster a culture where developers, IT operations teams, QA teams, and other stakeholders actively collaborate and communicate throughout the development process. -2. **Automation**: Increase efficiency and minimize errors by automating repetitive tasks like testing, deployment, and infrastructure provisioning. -3. **Continuous Integration and Continuous Delivery (CI/CD)**: Implement automated pipelines to test, integrate, and deploy code quickly and reliably. -4. **Metrics and monitoring**: Use real-time monitoring and analytics to identify issues, optimize performance, and asses system health. -5. **Customer-centric focused**: Center development and operations processes around delivering value to customers with high-quality releases that meet their needs. -6. **Continuous improvement**: Establish a culture of seeing the DevOps process as not a one-time thing but rather a continuous process that promotes a feedback loop and learns from success and failure. -7. **Infrastructure as Code (IaC)**: Treat infrastructure provisioning and maintenance as code to enable version control, scalability, and reproducibility. - -When you adopt DevOps in your development process, it offers numerous benefits, such as: - -- Accelerating software releases with streamlined processes and automation. -- Reducing operational costs through efficient workflows and fewer bottlenecks. -- Improving software quality with automated testing to reduce bugs and enhance reliability. -- Resolving issues faster using continuous monitoring and real-time system insights. -- Enhancing security by integrating security practices into the development lifecycle (DevSecOps). -- Gaining a competitive advantage through faster innovation and the ability to adapt quickly to market changes. - -Both DevOps and Agile offer numerous benefits that help you and your team build robust and scalable applications. But when should you choose one over the other? And what types of projects are best suited for each approach? - -Let's dive into their usage and application next. - -## Identifying when to use Agile vs. DevOps - -The table below summarizes when to use Agile and DevOps: - -| **Aspect** | **When to Use Agile** | **When to Use DevOps** | -| ------------------------ | ------------------------------------------------------------------------- | ---------------------------------------------------------------------------- | -| **Focus** | Building software step by step, improving as you go. | Combining development and operations for faster and smoother delivery. | -| **Team Structure** | Small teams working closely together, focusing on quick updates. | Developers and operations teams working as one from start to finish. | -| **Goal** | Deliver small, working parts of the project quickly and get feedback. | Deliver updates quickly and keep systems running smoothly. | -| **When Changes Happen** | Use Agile when project needs are likely to change often. | Use DevOps to handle changes quickly without breaking the system. | -| **Project Size** | Good for small to medium projects where teamwork and flexibility are key. | Good for large or complex projects where automation and speed are important. | -| **Release Timing** | Use Agile when you want planned updates (e.g., every two weeks). | Use DevOps when updates need to be released continuously. | -| **Tools and Automation** | Relies on planning tools like boards or trackers, with some automation. | Uses lots of automation tools to test, deploy, and monitor systems. | -| **Customer Involvement** | Use Agile when you need frequent feedback from customers. | Use DevOps when customers expect reliable and fast updates. | -| **Example Projects** | Developing a new app or adding new features to a product. | Running large systems or releasing updates to software quickly and often. | - -## Focus - -If your workflow is to develop software in small, manageable parts, Agile will be ideal for you. For example, if you're developing a new application, you can release the core features first, get feedback, and add more functionality over time. - -DevOps, on the other hand, is perfect for delivering software quickly and maintaining its stability. For example, if you're managing a large-scale ticketing platform, DevOps ensures updates without downtime. - -## Team structure - -Agile works well with small teams of developers, designers, and testers where everyone can collaborate closely. For instance, if you're building a product for a startup, Agile methodology guarantees everyone is aligned. - -In contrast, DevOps facilitates collaboration between development and operations teams to manage the entire process, from writing code to running in production. - -## Project size and change frequency - -Agile is well-suited for small to medium projects that want to launch their minimum viable product (MVP) or add new features to an existing platform that accounts for customers' needs along the way. - -DevOps, in contrast, is good for large or complex projects that involve infrastructure, automation, and scalability. - -## Project delivery - -Agile uses sprint (a time-boxed iteration) for planned updates, which makes it ideal for projects that deliver new features frequently (e.g., every two weeks). Agile helps you stay organized and on schedule. - -DevOps doesn't use intervals like Agile, and it allows you to release updates as soon as they're ready. - -## Customer involvement - -Agile works best when you need regular feedback from the customer at every stage of the development. In contrast, DevOps is better suited for scenarios where customers prioritize fast responses and high uptime. - -## Usage of tools and automation - -Agile keeps things simple by using tools like Jira and Trello to focus on planning and collaboration rather than heavy automation in DevOps, which relies on tools like Jenkins, Docker, and others that automate testing, deployment, and monitoring. - -| **Agile** | **DevOps** | -| ------------------------------- | -------------------------- | -| Jira (project management) | Jenkins (CI/CD automation) | -| Trello (task tracking) | Docker (containerization) | -| Confluence (team collaboration) | Kubernetes (orchestration) | - -As a rule of thumb, use the summary table below to decide when starting a new project or expanding existing ones. - -| **Factor** | **Agile** | **DevOps** | -| ---------------------------------------------- | --------- | ---------- | -| **Small, cross-functional teams?** | ✅ Yes | ❌ No | -| **Large teams requiring IT operations?** | ❌ No | ✅ Yes | -| **High variability in project complexity?** | ✅ Yes | ❌ No | -| **Multi-stage delivery pipelines?** | ❌ No | ✅ Yes | -| **Rare updates (deployment frequency)?** | ✅ Yes | ❌ No | -| **Frequent releases (deployment frequency)?** | ❌ No | ✅ Yes | -| **Need for automation (CI/CD or monitoring)?** | ❌ No | ✅ Yes | - -## Hybrid Situations: Combining Agile and DevOps - -In complex projects, blending Agile's adaptability with DevOps' automation and deployment efficiency can produce the best results for you and your team. Below are some use cases where adopting both methodologies proves most beneficial: - -- Large enterprise applications -- Mobile application development -- Microservices architecture -- Artificial Intelligence (AI) and Machine Learning (ML) projects - -**Large enterprise applications** - -If you're building a large enterprise application, you can break down feature development into smaller tasks and prioritize them in sprints using Agile. At the same time, DevOps helps maintain smooth delivery without downtime by automating testing, integration, and deployment. For instance, if you're managing a video streaming service, you can use Agile to plan features like personalized recommendations and DevOps to deploy them continuously to users. - -**Mobile application development** - -Mobile app development and maintenance involve fixing bugs, adding new features, and ensuring compatibility with new devices. Agile software development methodology can streamline feature iterations, while DevOps facilitates rapid updates across app stores. For instance, if you're building a fintech app that demands rapid feature development alongside robust security and reliability, Agile can help you build and iterate features efficiently. Meanwhile, DevOps can automate compliance checks, testing, and secure deployments to maintain quality and trust. - -**Microservices architecture** - -Microservices involves breaking down applications into smaller, independent services that can be developed and deployed separately. This approach aligns closely with Agile, as you can enable development teams to manage individual services, while DevOps facilitates seamless integration between these services and delivery. - -**AI and ML projects** - -Training, testing, and deploying AI models is an iterative process essential for keeping the system up to date. Agile practices can help you manage the iterative development of models and features, while DevOps can automate the deployment pipelines for updates and ensure effective monitoring in production. - -The faster deployment cycles, higher customer satisfaction, and stable releases achieved through the combination of Agile and DevOps stem from the shared values between these software development methodologies. Let's explore these similarities further. - -## Similarities between Agile and DevOps - -Agile and DevOps are distinct methodologies, but they share some similarities in their goals, approaches, and principles for software development process and delivery. Below are some key similarities between Agile and DevOps: - -![Agile and DevOps Similarities](https://assets.roadmap.sh/guest/similarities-between-agile-and-devops-9c79k.png) - -- Both emphasize collaboration between developers, testers, and other stakeholders to break down silos and foster teamwork. -- They prioritize delivering value to customers. -- Both advocate for working in smaller chunks rather than completing the project in one big cycle. -- They align in their focus on shortening the development cycles and reducing the time to market. -- Both promote continuous learning and process optimization. -- Both encourage the use of automation tools to enhance processes and reduce manual tasks. -- Both Agile and DevOps cultures require a shift towards openness and shared responsibility. - -While Agile and DevOps share common similarities, they also differ in focuses, principles, and practices. Let's explore these differences next. - -## Differences between Agile and DevOps - -Below are some key differences between Agile and DevOps: - -- Agile primarily focuses on the development phase with an emphasis on iterative development and continuous feedback, while DevOps focuses on the entire software lifecycle by bridging the gap between development and operations. -- Agile's core principle is customer-centric and welcomes changing requirements even late in the development process, while DevOps principle is to automate repetitive tasks, strive for efficiency in the delivery pipeline, and maintain reliable systems. -- The approach Agile uses to measure success is through speed, quality of software development, and customer satisfaction. DevOps, on the other hand, uses metrics like deployment frequency, mean time to recovery (MTTR), lead time for changes, and system reliability. -- Agile promotes a culture of collaboration and adaptability among the development team, while DevOps promotes a culture of shared responsibility and accountability across the development and operations team. - -## Next steps - -Agile and DevOps differ in their approaches, addressing distinct aspects of software delivery. Agile is best suited for small to medium projects that change frequently and require a high degree of adaptability. In contrast, DevOps excels in medium to large projects where efficiency and reliability are paramount. Ultimately, the approach you choose depends on factors such as project complexity, release frequency, and team size. - -If you're considering adopting DevOps at any stage of your development process, explore our comprehensive [DevOps roadmap](https://roadmap.sh/devops) for actionable steps and valuable resources to get started. \ No newline at end of file diff --git a/src/data/guides/devops-vs-developer.md b/src/data/guides/devops-vs-developer.md deleted file mode 100644 index 0ed79c988..000000000 --- a/src/data/guides/devops-vs-developer.md +++ /dev/null @@ -1,261 +0,0 @@ ---- -title: 'DevOps Vs. Developer: What Are The Key Differences?' -description: 'Understand the differences between DevOps and Developer roles. Find out which career aligns with your skills and future goals.' -authorId: william -excludedBySlug: '/devops/vs-developer' -seo: - title: 'DevOps Vs. Developer: What Are The Key Differences?' - description: 'Understand the differences between DevOps and Developer roles. Find out which career aligns with your skills and future goals.' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-vs-developer-yv25p.jpg' -isNew: false -relatedGuidesTitle: 'Other Guides' -relatedGuidesId: devops -type: 'textual' -date: 2024-12-17 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![DevOps engineers vs software developers compared](https://assets.roadmap.sh/guest/devops-vs-developer-yv25p.jpg) - -In the software development process, the main task of developers is to create, test, and deploy software applications. However, the increasing complexities of software projects have led to the creation of new roles, such as DevOps engineers. - -For hiring managers, understanding the distinct roles and contributions of DevOps and software developers can streamline the recruitment process, ensuring the right talent aligns with project needs. Career seekers, on the other hand, can make informed decisions by comparing the responsibilities and growth potential in each path. - -DevOps and software developers contribute to software development yet differ in various aspects and roles. DevOps is a broader framework that involves development and operations teams working together to modernize the **s**oftware **d**evelopment **l**ifecycle (SDLC). However, software development teams work exclusively on development tasks. - -Both career paths have their own roles and benefits, yet most people wonder: Who is paid more, a [DevOps](https://roadmap.sh/devops) or a [full stack developer](https://roadmap.sh/full-stack)? Some people even have a misconception that DevOps is a bit more difficult than software development. Anyone planning to choose a career path between the two must clearly understand their roles and core responsibilities. - -In this guide, I’ll break down the key differences between DevOps and software developers. Additionally, you’ll get a quick overview of salary insights for both roles and gain an understanding of new advancements and technologies that are set to shape the future of both fields. - -The table below summarizes the key differences between a DevOps engineer and a software developer. - -| **Job Title** | **Software Developer** | **DevOps Engineer** | -| ----------------------------------- | -------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **Key Tasks** | Software developers generally create codes, run tests, and manage deployment in small teams. | DevOps engineers automate tasks, manage infrastructure, and monitor and troubleshoot issues. | -| **Collaboration and Team Dynamics** | Communication is mostly restricted to software functionalities. | Communication and collaboration go beyond the development process, i.e., conveying technical issues, gathering feedback from different teams, discussing potential impacts, etc. | -| **Responsibilities and Processes** | Limited to writing, testing, and debugging codes. | Automate workflows and orchestrate SDLC. | - -## How do a DevOps engineer and a software developer differ? - -A [DevOps engineer](https://roadmap.sh/devops/devops-engineer) focuses on automating infrastructure, managing deployments, and ensuring smooth integration between development and operations, while a software developer primarily writes code to build applications or features. - -![DevOps Engineer vs Software Developer](https://assets.roadmap.sh/guest/devops-engineer-vs-software-developer-qodix.png) - -A software developer creates quality code and builds software applications. They focus primarily on designing, development, application testing, and software maintenance. - -Some may specialize in frontend, backend, or even full stack development, giving them the flexibility to manage comprehensive software projects. - -For HR professionals, if your organization wants to improve operational efficiency and teamwork across departments, hiring a DevOps engineer could be a good choice. On the other hand, if your focus is on innovation and coding, a skilled software developer or full stack developer might be the better option. - -A skilled software developer knows the right tools and programming languages essential to build applications. Some experts even have [frontend](https://roadmap.sh/frontend), [backend](https://roadmap.sh/backend), or [full stack](https://roadmap.sh/full-stack) development experience. - -However, DevOps is a broader field that involves collaboration between the development and IT operations teams. Software developers majorly focus on writing codes, whereas DevOps ensures that the generated code works well. - -Some of the key tasks of a software developer include: - -- **Coding and development:** using different programming languages and frameworks to create clean code for your software. -- **Testing:** running tests to get more clarity on whether the implemented code functions correctly and guarantees smooth software performance. -- **Deployment**: While traditionally managed by the DevOps team, many developers now handle deployment processes, especially in smaller teams. - -On the other hand, DevOps engineer's key tasks include: - -- **Automation DevOps**: automating all the tasks from testing software to deploying CI/CD pipelines. -- **Infrastructure management**: facilitating collaboration and managing the entire infrastructure to ensure smooth application performance. -- **Monitoring and maintenance**: using monitoring tools to track issues and application performance in real time. - -## Importance of collaboration with cross-functional teams in both roles - -Both developers and DevOps engineers need to collaborate with cross-functional teams for a smooth workflow. - -Working with cross-functional teams is essential for both software developers and DevOps engineers. These interactions streamline workflows, improve communication, and ensure that each department's needs and constraints are understood throughout the project lifecycle. - -**Hiring resource tip:** DevOps engineers may be more suitable for roles requiring collaboration across departments, whereas software developers, including full-stack developers, are better suited to focused development tasks. - -**Career insight -** As a software developer, your interaction might be limited to the design, development, and testing phases. However, moving toward DevOps expands your scope to include operational aspects, which could be beneficial for long-term career growth. - -Software developers need to constantly communicate with other developers on the team, designers, and even quality assurance(QA) engineers to ensure the delivery of high-quality software. Their collaboration is primarily focused on software functionalities. In the early stages of the traditional software development process, developers were not very involved in how their code operated in production. A separate department, i.e., the IT operations team or deployment manager, was responsible for tracking application deployment. - -![Collaboration and team dynamics](https://assets.roadmap.sh/guest/collaboration-and-team-dynamics-k302s.png) - -In the case of DevOps, things are completely different. Their collaboration with cross-functional teams goes beyond the development process. The DevOps concept was introduced to bridge the gap between development and operations for smooth workflow and rapid delivery. The development and IT operation team need to frequently communicate and provide quick feedback on different aspects for efficient software performance and deployment of code changes. - -The incorporation of DevOps practices will enable organizations to improve their product quality, automate complex and repetitive tasks, detect errors in real time, and deploy new features in less time. - -Let’s understand more about software developers and DevOps engineers' responsibilities and processes. - -## Key differences in responsibilities and processes - -**Software developer** -The primary responsibility of a software developer is to write, test, and debug code. The development team also looks after the software application's maintenance and ensures the final product aligns with your business goals and customer needs. - -Apart from writing code, software developers are also responsible for preparing technical documentation, troubleshooting issues in real time, and managing security issues. A software development team even collaborates and works closely with product managers, designers, and stakeholders to turn the basic idea into a high-quality software product. - -Another critical aspect of software developer role is participating in code reviews and providing constructive feedback to team members, contributing to overall code quality and team growth. - -**DevOps engineers** -The primary responsibility of a DevOps team is to automate the entire testing and deployment process. By automating workflow, organizations can ensure faster software delivery. - -The professionals use a wide range of DevOps tools to automate and orchestrate SDLC. They also monitor software performance via different monitoring tools like Prometheus, Grafana, Datadog, etc., provide rapid feedback loops, and troubleshoot errors and code bugs in real time. - -DevOps job role is not limited to deployment but is a continuous process that looks after various aspects to deliver highly scalable and secure software. - -Once you are clear with the responsibilities that come under both job profiles, it is also important to get clarity on the different career paths you can choose from and their pay scale. This will help determine which one can bring you more growth and success in the future. - -## Career paths and salary insights for software developer - -Job titles for software developers may vary based on the company and experience level. These include software engineers, software architects, and full stack developers. - -For career seekers, a full stack developer handles both frontend and backend tasks, making them highly versatile. This role offers a balanced career path for those looking to build comprehensive expertise in development. - -A DevOps engineer, meanwhile, is pivotal for organizations adopting modern frameworks like CI/CD pipelines and cloud infrastructure. - -For hiring managers, offering competitive salaries and clearly defined roles for full stack developers and DevOps engineers can attract top talent. Highlighting growth opportunities within your company can also increase retention rates. - -Below are some common job titles for software developers, along with salary insights for each role: - -- Full stack developer -- Software engineer -- Software architect - -**Full stack developer** -A full stack developer is a computer professional responsible for managing both the frontend and backend of your software. Full stack developers handle all aspects of database management, analysis, user interface design, code testing, and user communication. - -Based on my experience, currently, the salary of a junior full stack developer with (**0-1 year of experience**) ranges between **$61,000-$99,000**. A mid-level developer with **1 to 3 years of experience** can earn between **$67,000 and $106,000**. However, a senior full stack developer with **2 to 4 years of experience** can expect a salary ranging from **$72,000 to $113,000** in the United States. - -If you're looking to dive deeper into the specifics between full stack development and other technical roles, check out our detailed comparison of [DevOps vs full stack developer.](https://roadmap.sh/devops/vs-full-stack) It outlines how these professionals contribute to different aspects of the software development lifecycle, offering clarity for career seekers and hiring managers. - -**Software engineer** -Software engineering is another popular job profile that involves a wide range of domain options, such as mobile apps, frontend, backend, native apps, web development, etc. Software engineering professionals use engineering principles, new technologies, and programming languages to develop software applications and operating systems for end users. - -Have a look at our guide that compares [full stack developers vs software engineers](https://roadmap.sh/full-stack/vs-software-engineer), which will help you understand their roles and determine which aligns best with your skills and aspirations. - -The salary of a software engineering job profile ranges between $65K-$325K ([source](https://builtin.com/salaries/dev-engineer/software-engineer)). A junior software engineering job position can make up to **$89,247**, while a senior software engineer's average salary may come to around **$152,400.** - -**Software architect** -A software architect is another demanding position in software development projects. These professionals are responsible for some of the critical tasks that are highly complex in nature. They work on software functionality, security, performance, and other key aspects like architectural prototyping and quality assurance. - -Most software developers apply for software architect role because it is challenging and high-paying. A software architect's estimated [average salary will be $140,379.](https://www.payscale.com/research/US/Job=Software_Architect/Salary) - -## Career paths and salary insights for DevOps engineer - -The following are the job titles for DevOps engineer with their salary insights: - -- Cloud engineer -- Infrastructure engineer -- DevOps architect - -**Cloud engineer** -Cloud engineers are IT professionals who design, plan, and manage tasks related to cloud computing. These DevOps professionals help with cloud migration, maintain cloud resources, configure cloud infrastructure components, design scalable cloud applications, monitor cloud services and platforms, and more. - -In 2024, the estimated average salary of a cloud engineer ranges around **$116,252**, depending on experience, location, and specific skills in demand. - -**Infrastructure engineer** -Infrastructure engineers help assess existing software and project improvements. The infra engineer uses the latest techniques and technologies to maintain IT infrastructure and ensure seamless functioning. Further, they monitor system performance, troubleshoot errors, administer automated workflow tools, etc. - -In 2024, the [average salary](https://www.fieldengineer.com/skills/what-is-an-infrastructure-engineer) of a infrastructure engineer is between **$79,480** ([Payscale](https://www.payscale.com/research/US/Job=IT_Infrastructure_Engineer/Salary)) and **$115,606** ([ZipRecruiter](https://www.ziprecruiter.com/Salaries/Infrastructure-Engineer-Salary)), depending on factors such as geographic location, years of experience, and expertise in specific cloud platforms. - -**DevOps architect** -Another demanding position for DevOps engineers. These experts play an important role in implementing DevOps practices, facilitating proper integration and continuous deployment. They develop the overall DevOps strategy, select appropriate automation tools, manage the CI/CD pipeline, and cultivate a culture of collaboration among development and operation teams. - -In 2024, the [average salary of a DevOps architect](https://www.talent.com/salary?job=devops+architect) is estimated to be around $150,000, depending on industry, experience, geographic location, and specific cloud technologies or tools expertise. - -Now, if you have decided which career path you would like to pursue, let's get a quick insight into the skills essential for both job profiles. - -## Essential skills for aspiring DevOps engineers and software developers. - -**Top skills required for software developers** -Applying for software development job titles is no longer as simple as it used to be. Changes in the technology-driven workforce have resulted in companies looking for highly specialized individuals with the right set of skills and knowledge. Here are the top skills that IT decision-makers look for in an individual when hiring software developers for their organization: - -- **Programming languages** - A developer in an organization may work on a wide range of projects using different programming languages, so it's essential to have knowledge of multiple languages to develop an app or software. [JavaScript](https://roadmap.sh/javascript), [Go](https://roadmap.sh/golang), [Java](https://roadmap.sh/java), and [Python](https://roadmap.sh/python) are a few of the most commonly used languages. - -- **Database administration** - Database administration and management are other skills most organizations look for in a software developer. Developing a web application or software without database expertise is challenging, so it's essential to have a solid understanding of Oracle, MySQL, and other popular database management systems. - -- **Data structures and algorithms** - Another essential skill set is a good knowledge of [data structures and algorithms](https://roadmap.sh/datastructures-and-algorithms). A data structure is an organizing and storage format used in computer science to provide effective access to data. This helps software developers analyze massive codebases and identify errors more quickly. - -- **Secure coding practices** - IT decision-makers make sure that the selected software developer can build secure apps. This person, basically, uses secure coding practices and pays attention to details. - -- **Problem-solving and communication** - Developer’s core responsibility is to find solutions to issues. Developers can face various challenges when building an app or software, such as broken codes, errors, etc. One can analyze and fix the problem faster with problem-solving skills and creativity. Also, the ability to communicate with cross-team members and clients is another key factor often considered - when hiring software developers. - -- **Version control systems** - Version control is an essential skill for any software developer, as it enables collaboration, tracks changes, and ensures code integrity across teams and projects. Modern development workflows heavily rely on version control systems to manage source code effectively. - -**Top skills required for DevOps engineers** -The demand for DevOps engineers is constantly rising. Many organizations are looking to hire DevOps engineers with the right skills and talent. The applicant must have expertise in soft and technical skills in their roles. Hiring DevOps Engineers requires the following skills: - - -- **Coding and scripting** - DevOps engineers must have the two most important skills: coding and scripting. The DevOps professional must also have a good command of different programming languages for writing code. - -- **Linux** - Many organizations use Linux over other operating systems because of its flexibility, security, and stability. DevOps engineers must be clear with the fundamentals of Linux. - -- **System administration** - Another key skill that tech teams look for in a DevOps engineer is the ability to manage servers and troubleshoot issues faster. Experience in user administration, software installation, and system monitoring are a few skills essential to upkeep a robust infrastructure. - -- **Software testing** - With this skill, DevOps engineer can identify faults and bugs faster and improve codebase quality. A DevOps engineer must be proficient in using different testing methodologies. - -- **Automation** - Infrastructure provisioning, configuration management, automation of CI/CD pipelines, or application deployment are a few areas that DevOps must know how to use and implement. - -- **Cloud computing** - In the modern DevOps landscape, cloud computing plays an important role. DevOps engineers must be skilled in deploying, managing, and maintaining applications on cloud platforms like AWS, Microsoft Azure, and Google Cloud Platform (GCP). - -Here are a few DevOps engineer certifications and training courses you can apply for: - -- AWS-certified DevOps engineer -- Google professional DevOps engineer -- Continuous delivery and DevOps -- DevOps culture and mindset - -## Future trends in DevOps culture - -Software development has been in practice for a long time. Despite advancements and innovations in technology, the software development process remains essential. However, the introduction of DevOps had an impact on modern software development: - -- **Faster time-to-market**: With DevOps principles, organizational teams are able to ensure faster software delivery. -- **Enhanced collaboration**: DevOps enabled better communication and collaboration between development, operations, and other cross-functional teams. -- **Faster resolution and quality**: Automation tools helped gain faster feedback on code quality and reduce errors, which improved overall software quality and performance. - ![Future trends in DevOps culture](https://assets.roadmap.sh/guest/future-trends-indevops-culture-mczvt.png) - -Organizations constantly adapt new digital techniques and technologies to ease the task and deliver quality software design to end-users. In fact, [Puppet’s State of DevOps report](https://www.puppet.com/resources/state-of-platform-engineering) clearly stated that developer needs are changing. Many other DevOps enterprise summits have discussed how these transformations will sooner or later further evolve DevOps culture as well as impact software developers. Some of the key trends that may impact the future of DevOps include: - -- **AI and machine learning** - Integration of AI and machine learning into DevOps processes is becoming a trend. With this incorporation, all the major tasks, such as code releases and analysis, software deployment, and application software monitoring, will become faster and more reliable. By enabling quicker identification and resolution of issues, they also improve predictive analytics, aiding in resource planning and performance optimization. - -- **DevSecOps** - An approach to software development that focuses on identifying and reducing security vulnerabilities in software systems. It strongly emphasizes working together to automate procedures and expedite deployment across development, operations, and security teams, ensuring secure, seamless, and faster deployments. - -- **GitOps** - GitOps is a powerful framework software development teams use to automate deployment processes and better manage infrastructure. The approach is based on DevOps practices, such as version control systems and continuous integration and delivery. - -- **Infrastructure as Code** - Another DevOps practice that uses configuration files for infrastructure management and provisioning. It not only saves time but also improves accuracy by eliminating manual processes. - -- **Low-code and no-code development** - These platforms are making DevOps accessible by enabling non-technical users to participate in development processes. By integrating with DevOps pipelines, these tools accelerate application delivery while maintaining high standards of quality and security. - -## Conclusion - -Have you made up your mind? Which one will you choose as your career path—a DevOps engineer or a software developer? - -For hiring managers, decide whether your organization needs operational efficiency (DevOps) or innovative software solutions (Developers). For career seekers, choose based on your interest in infrastructure challenges or creative coding. - -It can be challenging to decide between the two prominent profiles. However, you must pursue your interests and career goals. Here is a quick recap of what we covered in this post: - -Software development is more about design and development. If you are interested in coding or building new features, you can opt for software developers. You can choose from the different job titles available under software developers, i.e., [frontend developers](https://roadmap.sh/frontend), software architect, etc. - -DevOps engineers, on the other hand, is a process framework that involves challenges with access to automation, configuration management, infrastructure management, and other areas essential for smooth system performance and functioning. They must be familiar with Continuous integration and delivery, and other above-listed skills. This field also comprises roles and positions, such as Cloud Engineer, Infrastructure Engineer, etc. - -Whatever path you choose, you can get more insights and guidance from our [roadmaps](https://roadmap.sh/). Register with us and gain access to a wide array of insightful guides, covering different roles and diverse topics. If you want detailed insights on any specific role, join the [Discord community](https://roadmap.sh/discord) and stay informed! \ No newline at end of file diff --git a/src/data/guides/devops-vs-devsecops.md b/src/data/guides/devops-vs-devsecops.md deleted file mode 100644 index 9a750cd53..000000000 --- a/src/data/guides/devops-vs-devsecops.md +++ /dev/null @@ -1,231 +0,0 @@ ---- -title: 'DevOps vs DevSecOps: Key Differences and Best Fit' -description: 'DevOps vs DevSecOps: Learn the key differences, benefits, and how to choose the best approach for your needs and applications.' -authorId: ekene -excludedBySlug: '/devops/vs-devsecops' -seo: - title: 'DevOps vs DevSecOps: Key Differences and Best Fit' - description: 'DevOps vs DevSecOps: Learn the key differences, benefits, and how to choose the best approach for your needs and applications.' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-vs-devsecops-3drth.jpg' -relatedTitle: 'Other Guides' -relatedGuidesId: 'devops' -isNew: false -type: 'textual' -date: '2025-01-17' -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![DevOps vs DevSecOps comparison guide](https://assets.roadmap.sh/guest/devops-vs-devsecops-3drth.jpg) - -Over the years, the demand for high-quality software and resilient systems has grown significantly. Businesses are under immense pressure to deliver software faster than ever. However, rushing development often comes with trade-offs, such as increased security risks that can compromise entire systems. - -Traditional development practices struggled to keep up with the need for both speed and security, creating a critical challenge for organizations. To address the challenge of balancing rapid software delivery with the need for robust security and quality, two models were introduced: DevOps and DevSecOps. - -[DevOps](https://roadmap.sh/devops/devops-engineer) focuses on streamlining the development and operations lifecycle to deliver software quickly. DevSecOps integrates security practices into the DevOps pipeline, prioritizing security from the start and throughout the entire development process. - -In this blog, you will learn about the main purpose and role of DevOps and DevSecOps. You will also explore a comparison between the two, helping you determine which approach is right for your needs. Understanding the key differences and benefits is essential to choosing the right model, so keep reading! - -Below is a quick comparison table of DevOps vs. DevSecOps for easier reference: - -![DevOps vs DevSecOps](https://assets.roadmap.sh/guest/comparison-table-of-devops-vs-devsecops-wcai5.png) - -## DevOps vs DevSecOps: How are they different? - -Choosing between DevOps and DevSecOps can determine whether your software is fast—or secure from the start. - -While both approaches aim to enhance collaboration and efficiency in software development, DevSecOps incorporates security practices early in the development cycle, unlike DevOps, which often addresses security issues at a later stage. - -DevOps is primarily focused on improving collaboration between design, development, and operations teams to speed up software delivery. The core idea is to remove bottlenecks and inefficiencies in the development pipeline. DevOps engineers are skilled in coding, automation, and system administration, and they focus on delivering high-quality software with minimal errors, often through continuous integration and continuous delivery (CI/CD). - -![Understanding DevOps vs DevSecOps](https://assets.roadmap.sh/guest/understanding-devops-vs-devsecops-v9tkn.png) - -On the other hand, DevSecOps brings security into the equation by integrating cybersecurity practices throughout the development process. This approach arose to address increasing cyber threats by embedding security checks at every phase of the software development lifecycle (SDLC). While DevOps ensures quick software delivery, DevSecOps emphasizes secure and compliant software delivery by shifting security considerations to the left of the development timeline, ensuring that vulnerabilities are detected early. - -## Security in DevOps vs. DevSecOps: A Closer Look at Processes and Tools - -One of the key differences between DevOps and DevSecOps lies in how they handle security. -In DevOps workflows, security testing typically occurs near the end of the development cycle, during quality assurance or post-deployment. Security measures, such as patches or vulnerability scanning, are often applied as part of the release process. This can result in delayed launches or costly remediation efforts if critical issues are discovered late in the pipeline. - -![Role of security in DevOps and DevSecOps](https://assets.roadmap.sh/guest/role-of-security-rj7j1.png) - -DevSecOps, on the other hand, focuses on strengthening deployment security and maintaining data protection and compliance by tracking issues as they arise. This approach uses both shift-left and shift-right security testing strategies. Shift-left testing involves identifying security vulnerabilities early in the development process, even before code is merged. - -Tools like static application security testing (SAST), dynamic application security testing (DAST), and dependency checkers are embedded into CI/CD pipelines to catch publicly disclosed vulnerabilities. Additionally, automated scanners and code analyzers ensure that potential risks are flagged before reaching production. - -For example, in a DevOps environment, a team may identify security vulnerabilities only after a routine code audit or during the final phase of testing. However, in a DevSecOps setup, automated security checks would be integrated into the CI/CD pipeline, flagging issues in real time before code is deployed, saving time and mitigating risks. - -### Shift-Left and Shift-Right Strategies - -[Shift-left security](https://roadmap.sh/devops/shift-left-testing) is a key component of DevSecOps. With early security testing (shift-left security), DevSecOps helps identify risks and prevent product compromise. Addressing errors during the production phase is far more cost-effective than fixing them after deployment. Additionally, continuous security testing reinforces compliance with industry standards. - -A real-world example of the consequences of delayed security integration is the [2017 Equifax data breach](https://archive.epic.org/privacy/data-breach/equifax/). A known vulnerability in the Apache Struts framework was left unpatched, resulting in the exposure of sensitive customer data for over 147 million people. Had shift-left security practices been in place, experts could have flagged the outdated library during the early stages of development and prevented breach. - -Shift-right testing is equally important as it helps developers detect security threats and fix issues in real time. Delays in security threat detection can impact product integrity and customer trust. - -For instance, imagine your organization is working on financial application design and creation, in which security processes and tests are supposed to run during the final phase. - -In such a case, issue detection at the final phase can result in a product launch delay. This might also result in higher costs. And if, to avoid a launch delay, you introduce the product, the unresolved vulnerabilities can hamper your reputation and erode customer trust. - -DevSecOps understands the impact of security issues and thus recommend implementing shift-left and shift-right strategies that help reduce vulnerabilities and achieve faster time to market, thus saving your organizational reputation and customer trust. - -## How DevOps and DevSecOps affect business goals? - -DevOps and DevSecOps affects several key business such as time to market, customer satisfaction, operational efficiency and risk management. Here's how they affect these goals. - -1. **Time to market** - -DevOps speeds up product delivery by automating workflows, removing bottlenecks and enabling faster iterations. DevSecOps puts the necessary checks in place without derailing development timelines so businesses can maintain a regular release cadence and meet market demand. - -2. **Customer satisfaction** - -DevOps delivers frequent updates and new features to keep up with customer demand and improve user experience. DevSecOps builds on this by delivering secure and reliable products, reducing the risk of issues that will frustrate users. Both speed and reliability increases customer trust and loyalty. - -3. **Operational efficiency** - -DevOps makes tasks more efficient by removing duplication, eliminating manual intervention. DevSecOps adds to this by addressing risks early, avoiding rework or operational downtime. Together they reduce development costs and increase productivity. - -4. **Risk management** - -DevOps allows for faster iterations and deployments which can introduce risks if not managed properly. DevSecOps mitigates these risks by making security a core part of the development lifecycle. This proactive approach reduces the chance of breaches or compliance issues and protects the business's reputation and financials. - -## Core processes in DevOps and DevSecOps - -To accelerate the software development lifecycle, DevOps gives more attention to automation and collaboration. Monitoring in DevOps primarily focuses on performance, availability, and system uptime. Metrics like CPU utilization, application response times, and log aggregation form the foundation of DevOps monitoring strategies. Incident response, while essential, is reactive in nature—triggered only after an issue, such as a system crash or performance degradation, arises. - -DevSecOps runs security tests in all stages, thus adopting a more proactive approach. Continuous monitoring in DevSecOps goes beyond traditional metrics to include threat detection, vulnerability scanning, and compliance checks, focusing more on reducing risk and cost. They even use tools like SIEM (Security Information and Event Management) systems and cloud-native security platforms to detect threats in real time. Incident response in DevSecOps involves automated playbooks and AI-driven analysis to address vulnerabilities, often before they can escalate rapidly. - -In fact, DevSecOps even employs SAST and DAST strategies that help identify security vulnerabilities faster. Under SAST, professionals scans source code early to prevent vulnerabilities from entering production. Some of the common SAST tools are [SonarQube](https://www.sonarsource.com/products/sonarqube/) and [Checkmarx](https://checkmarx.com/). - -However, under the DAST strategy, professionals evaluate applications in their running state to identify vulnerabilities. Some of the common DAST tools are [OWASP ZAP](https://www.zaproxy.org/) and [Burp Suite](https://portswigger.net/burp) which help identify injection flaws and security misconfigurations. - -Interactive Application Security Testing (IAST), another practice that combines SAST and DAST, operates within the application runtime environment to provide detailed insights into vulnerabilities during testing and QA phases. - -These advanced testing methodologies—SAST, DAST, and IAST—not only enhance security within specific stages of development but also lay the groundwork for broader, innovative practices in continuous security monitoring. These emerging practices are redefining traditional monitoring and response strategies: - -### Real-time incident response - -DevSecOps uses tools that use AI and machine learning for real-time threat detection and mitigation. - -Examples of tools used for real-time response in DevSecOps are Splunk, Datadog Security Monitoring, and CrowdStrike Falcon which do AI driven threat detection and automated response to incidents. - -### Behavioral analytics - -Monitoring user and application behavior allows DevSecOps teams to detect anomalies such as data access or traffic patterns that are not normal which could be a breach. - -### Continuous compliance - -DevSecOps embeds security policies and regulatory standards into the development process for continuous compliance. Teams use automation tools like Policy-as-Code frameworks and compliance scanners to enforce and validate against standards like GDPR, HIPAA and PCI DSS. This way reduces compliance risks and makes auditing easier. - -## Moving from DevOps to DevSecOps - -The shift from DevOps to DevSecOps is a critical evolution for organizations aiming to integrate security seamlessly into the development lifecycle. Below is a guide to facilitate this transition, highlighting actionable steps, tools, and strategies for success. - -![Moving from DevOps to DevSecOps](https://assets.roadmap.sh/guest/moving-from-devops-to-devsecops-gimtv.png) - -### Understand your goals - -Before implementing changes or transitioning to DevSecOps, step back and clearly lay out your goals. What do you want to achieve after transitioning to the DevSecOps model? Are you looking to enhance your security model or need faster software deployment? Being specific with your goals will help you make informed decisions and develop a plan that aligns with your goals. - -### Current flow assessment - -Before transitioning to a new model, it is important to assess and change the existing workflow. Trace areas that demand or require improvement and attention. For example, check for proper coordination and communication among development, operations, and security teams. Are there any loopholes or faults in your current workflow? Is security feedback consistently integrated into development cycles? Identify gaps in communication by hosting cross-team retrospectives or root cause analyses to get more clarity of your situation. - -### Choose the right automation tool - -If workflow efficiency is an issue, the best solution is to implement automation tools. These common tools can reduce manual tasks, run faster code reviews, perform security tests, and provide quick deployment. These tools further enable professionals to focus on other key areas and tasks, such as fixing errors or initiating new features within the application. - -### Training your teammates - -Adopting a new model or practice demands educating the team members early about the new process and security concerns. Inform and educate your team members about the importance of security systems and how integrating them can improve their overall performance. You can also run training sessions or seminars to cover more about security guidelines and standards. Help them understand rising security concerns, how to fill gaps, and how to integrate security throughout the software development cycle. This step will further prevent confusion and problems from escalating in the future. - -Educating the team early prevents missteps and promotes ownership of the new processes. But. despite preparation, organizations often face challenges when transitioning to DevSecOps. Let us have a look at some common DevSecOps transition challenges. - -## DevSecOps transition challenges - -Remember, transitioning to DevSecOps is not as easy as it sounds. There are various challenges, but these can be managed with the right approach. - -![DevSecOps Best Practices](https://assets.roadmap.sh/guest/devsecops-best-practices-m6e21.png) - -Here are a few things to avoid in the transition period: - -### Wrong tool selection - -There is a wide range of security applications on the market, but make sure to select the right one that is relevant to your code and meets your requirements. Otherwise, you might find it hard to run in the long term. - -For guidance on selecting the right tools, refer to the **"Top tools and processes for a smooth transition"** section, where we highlight specific tools and best practices to facilitate a successful DevSecOps implementation. - -### Non-inclusion of operations and security teams - -Security tests are conducted at every phase of software production. Excluding your operations and security teams from the monitoring and tracking process limits the ability to identify and address faults and bugs effectively. Involving security experts from the start allows them to provide guidance on misconfigurations, tools, and best practices. - -### Speed over quality - -DevOps emphasizes quick software delivery, which can sometimes lead to insufficient attention to quality and security functionality. This may affect the user experience and your business reputation. Allocating more time and effort to ensuring quality and integrating security practices can help strike the right balance. - -### Code monitoring issues - -Since code constantly changes in software production, keeping an eye on it at all times can be challenging for some professionals. It is important to introduce new configurations, tools, and practices that can identify vulnerabilities in real time. - -## Top tools and processes for a smooth transition - -Further, let's have a look at how tools and processes can facilitate this transition while keeping agility and innovation intact: - -### Prioritize security-first CI/CD configurations - -CI/CD pipelines are the backbone of modern DevOps workflows. Embedding security into these pipelines guarantees vulnerabilities are identified and mitigated at every stage. So, incorporate Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST) tools. - -Further, you can use HashiCorp Vault or AWS Secrets Manager to manage sensitive information securely. Also, you can set policies to prevent critical vulnerabilities using tools such as Jenkins, GitHub Actions, or GitLab CI/CD. - -### Embrace infrastructure as code (IaC) - -IaC automates infrastructure provisioning, but it can also introduce risks if not properly secured. For a smooth transition, it is important to integrate security into IaC processes. You can invest in Terrascan to detect vulnerabilities in Terraform or implement [immutable infrastructure practices](https://devops.com/immutable-infrastructure-the-next-step-for-devops/) to reduce configuration drift. Further, you can conduct regular audits of IaC templates for misconfigurations. - -### Leverage advanced threat modeling - -Threat modeling makes certain that potential security risks are identified early. New tools and frameworks make this process more effective. Invest in AI-powered tools that automatically suggest mitigations for identified risks. - -## Will DevSecOps replace DevOps? - -No, DevSecOps will not replace DevOps; instead, it enhances it. Rather than being a replacement, DevSecOps is an augmentation of DevOps, bringing security practices into the development and operations workflow. While DevOps focuses on speed, collaboration, and efficiency, DevSecOps makes sure that security becomes an inherent part of these processes. For example, integrating security tools like Snyk or SonarQube into CI/CD pipelines helps organizations identify vulnerabilities early in the development lifecycle. - -The two are complementary rather than mutually exclusive. DevSecOps acts as a bridge, ensuring that security doesn't become a bottleneck while maintaining the agility of DevOps. This natural evolution addresses the growing need for secure software development without compromising agility. Let's further learn how DevSecOps will evolve in the future. - -## Future of DevSecOps - -As cybersecurity threats grow in sophistication and compliance regulations tighten, DevSecOps is poised to become the cornerstone of secure software development. In the recent [CrowdStrike Global Threat Report](https://www.crowdstrike.com/en-us/global-threat-report/), experts clearly stated that attacks hardly take a few minutes to succeed. The report also noted that CrowdStrike tracked over 230 adversaries leveraging the global adoption of cloud technologies for their attacks. - -Tackling these challenges demands strategic teamwork and technical expertise. Here's how DevSecOps is expected to evolve and why it is the future of secure DevOps: - -### Proactive threat mitigation - -DevSecOps is transitioning from reactive to proactive security. Predictive threat analysis, enabled by AI and machine learning, will play a crucial role in identifying security concerns and vulnerabilities before exploitation. For example, tools like CrowdStrike will become essential for analyzing attack patterns. - -### Integration with governance and compliance - -Stricter regulations such as GDPR, HIPAA, and CCPA are driving a compliance-first culture. DevSecOps will increasingly integrate automated compliance checks into CI/CD pipelines, facilitating adherence to global standards without manual intervention. - -### Rise of zero-trust architectures - -The adoption of zero-trust principles will reshape security frameworks. DevSecOps will integrate zero-trust policies into development environments, guaranteeing continuous authentication and access verification. This approach will strengthen security for microservices and API-driven architectures. - -### Cloud-native and container security - -With the surge in cloud-native applications, securing containers and serverless environments will be a top priority. Several tools are available that will enable seamless security integration into cloud workloads, addressing misconfigurations and runtime vulnerabilities. - -In fact, in terms of demand and salary, DevSecOps roles tend to offer higher salaries due to the specialized skill set and smaller talent pool. Soon, there will be more role demands for security teams, DevSecOps engineers, security automation specialists, and compliance analysts job profiles to integrate security systems throughout the SDLC. - -## Conclusion - -Now that you understand the differences between DevOps and DevSecOps, the choice comes down to your organization's specific goals and priorities. - -If speed and efficiency are your primary focus, DevOps is a great fit. However, if security is paramount, DevSecOps is the better choice. By embedding security into every stage of the development lifecycle, DevSecOps helps mitigate vulnerabilities while ensuring compliance and quality. - -Both methodologies hold significant value, but in an era of increasing cybersecurity threats, DevSecOps is becoming essential for organizations that prioritize secure innovation. - -To navigate these approaches effectively and align them with your long-term goals—such as scalability, compliance, and reputation—explore our comprehensive [DevOps roadmap](https://roadmap.sh/devops). It offers actionable insights to help you build a strategy that drives efficiency, security, and success. \ No newline at end of file diff --git a/src/data/guides/devops-vs-full-stack.md b/src/data/guides/devops-vs-full-stack.md deleted file mode 100644 index 61971838a..000000000 --- a/src/data/guides/devops-vs-full-stack.md +++ /dev/null @@ -1,217 +0,0 @@ ---- -title: 'DevOps engineer vs Full stack developer: Which is best?' -description: 'DevOps engineer vs Full stack developer: Compare the roles, required skills, and future prospects to make an informed career choice.' -authorId: ekene -excludedBySlug: '/devops/vs-full-stack' -seo: - title: 'DevOps engineer vs Full stack developer: Which is best?' - description: 'DevOps engineer vs Full stack developer: Compare the roles, required skills, and future prospects to make an informed career choice.' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-engineer-vs-full-stack-developer-jccsq.jpg' -relatedGuidesTitle: 'Other Guides' -relatedGuidesId: devops -isNew: false -type: 'textual' -date: 2024-10-17 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![DevOps vs Full Stack: Which suits you best?](https://assets.roadmap.sh/guest/devops-engineer-vs-full-stack-developer-jccsq.jpg) - -There are several roles in tech, two common ones being DevOps engineers and full stack developers. The software development industry relies heavily on full stack and DevOps development. - -DevOps aims at bridging the gap between developers and operation teams. This leads to an efficient and improved software delivery process. - -A [DevOps engineer's](https://roadmap.sh/devops) primary responsibility is creating automated processes to develop, test, deploy, and maintain software systems, while a full stack developer specializes in writing application code that covers both the user-facing side (frontend) and behind-the-scenes logic (backend). - -This guide explains what each role entails, addresses the motivations behind choosing either path, guides developers on choosing either path, and gives helpful tips for those who want to transition to DevOps engineering. - -![DevOps Process](https://assets.roadmap.sh/guest/devops-process-ajk6p.png) - -The key differences between DevOps engineers and full stack developers are summarized in the table below. - -| DevOps engineer | Full stack developer | -|-----------------|----------------------| -| Responsible for the smooth flow of code changes from development to production. | Focuses on end-to-end application development (both frontend and backend) | -| Uses monitoring tools to track the performance of deployed software and also identify issues and bottlenecks in the deployment process. | Writes unit, integration, and end-to-end tests for the application code and fixes bugs related to the code. | -| Focuses on automating processes and ensuring a software application runs reliably and flawlessly. | Handles the development of web applications or a software program (frontend and backend) | -| Familiar with [tools that aid task automation](https://roadmap.sh/devops/automation-tools), code testing and deployments. | Has expertise in various frontend and backend programming languages | -| Focuses more on the infrastructure management side of the whole development life-cycle, which includes managing networks and servers. | Could either focus on the frontend and backend web architectures. | -| Has an in-depth understanding of operations to ensure optimal software delivery. | Possess a basic knowledge of operations. | - -## DevOps engineer or full stack developer - -Full stack developers specialize in web development, while DevOps engineers **focus** on the smooth integration and delivery of software components. - -Both roles offer great career opportunities. DevOps engineers can work in different sectors and organizations occupying different capacities. - -Some DevOps specializations include code release manager, automation architect, DevSecOps engineer, etc. The same applies to a full stack engineer. As a full stack developer, you can work as a [frontend](https://roadmap.sh/frontend?r=frontend-beginner) or [backend](https://roadmap.sh/frontend?r=frontend-beginner) developer. - -DevOps developers and full stack developers are also in high demand. According to [Statista](https://www.statista.com/statistics/1367003/in-demand-it-roles/), full stack developers and DevOps developers are among the top technical positions demanded by recruiters worldwide in 2023. Indeed reported that the average salary of a [DevOps engineer](https://www.indeed.com/career/development-operations-engineer/salaries?from=top_sb) in the USA is $124,392, and that of a [full](https://www.indeed.com/career/full-stack-developer/salaries?from=top_sb) [](https://www.indeed.com/career/full-stack-developer/salaries?from=top_sb)[stack software developer](https://www.indeed.com/career/full-stack-developer/salaries?from=top_sb) is $124,120. - -Before deciding which path to follow, some introspection is encouraged, and some factors are to be considered. Some of the things to consider include: - -- Interest -- Strengths -- Willingness to continously learn new skills and technology - -### Interest - -Considering your interests before choosing which path to follow is helpful. Building a career takes time and hard work, and it is advisable to do that in something you are interested in. - -DevOps is probably the right choice if you are interested in automating repetitive tasks, servers and cloud management, containerization, monitoring, logging etc. - -On the other hand, if you are interested in writing application and domain code and enjoy seeing what you build and how users interact with applications directly and indirectly, then it is advisable to choose full stack development. - -### Strengths - -In addition to your interests, it is also helpful to consider what your strengths are. This would help you to decide what you can work on effortlessly and with less struggle. - -Do you find scripting easy? Are you able to grasp the complexities behind containerization and container orchestration? Do you spend more time writing infrastructure code than writing application and domain code? If your answer to these questions is yes, you should consider DevOps. - -Can you easily convert mock-ups to actual user interfaces? Do you find it fascinating to translate customer requirements into code? Are you able to interact with databases using SQL without much hassle? If yes, then it might be worth going for full stack development. - -### Willingness to continuously learn new skills and technology - -The DevOps and full stack fields continually evolve, and there is always something new. To be up to date, you have to be willing and open to learning constantly. This involves taking courses, reading articles, and getting updates on things happening in the tech field. - -Here is each role in detail to help you make a deeper consideration. - -## Who is a DevOps engineer? - -A [DevOps](https://roadmap.sh/devops) engineer who can also be referred to as a DevOps developer is an IT professional with knowledge of development and operations. The development part involves writing codes and scripts, while the operations part includes managing on-premises and/or cloud infrastructure and system infrastructure. - -In traditional software development, there is the challenge of having different teams working in silos. This siloed team structure makes it challenging to collaborate amongst teams, and the priorities and timelines of each team don't align with other teams. - -DevOps helps to bridge the gap between development teams and operations teams. DevOps experts often work in a collaborative surrounding where they can collaborate with software engineers and IT teams. - -![DevOps process](https://assets.roadmap.sh/guest/devops-lifecycle-simple-9lvkw.png) - -DevOps has some core principles that influence the effectiveness of development and operations. Some of these DevOps principles include: - -- [Automation](https://roadmap.sh/devops/automation) of the software development lifecycle -- Collaboration - -### Automation of the software development lifecycle - -This involves automating tests, builds, and releases of software versions, as well as tasks that can slow down the software delivery process. - -### Collaboration - -It breaks the silos across teams and enables collaboration and communication. This creates horizontal slices and enhances productivity across teams. - -A DevOps developer can use several programming languages for development. Some of them are [Python](https://roadmap.sh/python), Ruby, [Go](https://roadmap.sh/golang), and [Rust](https://roadmap.sh/rust). Also, bash scripts help to automate processes. - -Some organizations manage their own server infrastructure on-premise and deploy their applications on these servers. DevOps engineers are responsible for ensuring the servers run reliably and applications deploy successfully. - -Cloud computing has gained popularity, and many software applications are deployed on various cloud computing platforms. There are cloud solution providers like [Microsoft Azure](https://azure.microsoft.com/), [Amazon Web Services](https://roadmap.sh/aws), and [Google Cloud Platform](https://cloud.google.com/) who take care of the server infrastructure and are mostly more reliable than the on-premise solution. One significant benefit of the cloud solution is that it is on a pay-as-you-go basis, i.e., you pay for only the cloud resources you use. - -## Skills required to be a DevOps engineer - -DevOps engineers require soft and technical skills to succeed in their career path. The skills required include: - -- Knowledge of coding and scripting -- Knowledge of operating systems -- In-depth knowledge of containerization and orchestration -- Basic understanding of version control -- Understanding of monitoring, logging, and alerting systems -- Knowledge of cloud computing - -### Knowledge of coding and scripting - -Coding and scripting are essential skills every DevOps engineer should have. These skills are typically employed to automate repetitive tasks. Some of the recommended programming/scripting languages used in DevOps include [Python](https://roadmap.sh/python), [Go](https://roadmap.sh/golang), Ruby, [Rust](https://roadmap.sh/rust), and Bash. - -### Knowledge of operating systems - -DevOps engineers should have knowledge of operating systems. One common operating system used in DevOps is [Linux](https://roadmap.sh/linux). Having the fundamental knowledge of Linux is required, as many servers are Linux based. - -### In-depth knowledge of containerization and orchestration - -DevOps engineers should know how to use containerization tools to do their jobs effectively. Some common examples of containerization and orchestration tools include [Docker](https://roadmap.sh/docker) and [Kubernetes](https://roadmap.sh/kubernetes). - -**Basic understanding of version control and continuous integration and deployment** -A DevOps engineer should be able to manage and track code changes. This is done with the use of version control systems. Git is a common version control system - -Also, DevOps engineers should be familiar with continuous integration and deployment (CI/CD) tools that enable the automatic integration of code changes. Some common CI/CD tools are CirceCl and GitLab. - -### Understanding of monitoring, logging, and alerting systems - -Monitoring and logging are key aspects of the DevOps process, and it is expected that as a DevOps engineer, you have a good understanding of them. DevOps engineers use logging and monitoring systems to gather, analyze, and understand the system performance, and they set up alerts to be notified if the system state changes and needs to be attended to. - -### Knowledge of cloud computing - -DevOps engineers should have solid cloud computing skills. Recently, many applications have been deployed on the cloud. Third-party cloud providers mostly manage the cloud infrastructure. Some of the common cloud providers include [AWS](https://roadmap.sh/aws), Microsoft Azure, and Google Cloud Platform. - -## Who is a full stack developer? - -[Full stack developers](https://roadmap.sh/full-stack) are software developers with extensive frontend and backend development knowledge. Their role is to handle the complete web development process, from designing the user interface to building the server-side logic. - -The frontend of an application includes everything the user can see and interact with, i.e., the user interface. The backend consists of the things the user doesn’t see. These include the server-side and systems supporting the business logic. - -Full stack coders also use [DevOps tools](https://roadmap.sh/devops/tools). Depending on the project, a full stack developer may use DevOps technologies like GitHub and mongoDB to create software applications. - -Let's take a look at frontend and backend development in greater detail. - -### Frontend development - -It is concerned primarily with the user interface (UI) and user experience (UX). The common languages used in frontend development include HTML, CSS, and JavaScript. HTML defines the markup of the web page. CSS builds upon HTML and represents the style and format of the webpage. JavaScript is a programming language often used for frontend development and adds logic to your web page. You'll find an excellent guide and roadmap to learning [JavaScript](https://roadmap.sh/javascript) on roadmap.sh. - -![Frontend development](https://assets.roadmap.sh/guest/frontend-development-common-languages-25kzq.png) - -There are quite a few frontend frameworks out there. Some of the common ones are [React](https://roadmap.sh/react), [Vue](https://roadmap.sh/vue), and [Angular](https://roadmap.sh/angular). For a more detailed guide, look at the [frontend beginners roadmap](https://roadmap.sh/frontend?r=frontend-beginner) or the [advanced frontend roadmap](https://roadmap.sh/frontend?r=frontend). - -### Backend development - -It focuses on the application's functionality and *business logic*. Examples of backend components include data storage, security, and handling of business logic. - -Backend development mainly involves creating API endpoints consumed by the application's front end. Some common backend programming languages include C#, [Java](https://roadmap.sh/java), [Rust](https://roadmap.sh/rust), [Golang](https://roadmap.sh/golang), and [Python](https://roadmap.sh/python). Check out the [backend developer](https://roadmap.sh/backend) roadmap. - -![Backend development](https://assets.roadmap.sh/guest/backend-programming-common-languages-oyd3s.png) - -## Skills required by full stack developers. - -The necessary technical skills to required by full stack engineers include: - -- Knowledge of HTML, CSS, and [JavaScript](https://roadmap.sh/javascript)/[TypeScript](https://roadmap.sh/typescript). -- Knowledge of at least one JavaScript framework, e.g., [React](https://roadmap.sh/react), [Vue js](https://roadmap.sh/vue), [Angular](https://roadmap.sh/angular). -- Knowledge of at least one backend language. You can transfer your knowledge of JavaScript to backend development with [Node JS](https://roadmap.sh/nodejs). -- In-depth understanding of server-side rendering and web security. -- Knowledge of APIs. -- Understanding of database management systems and database architecture. - -## How to transition to DevOps - -Some fundamental knowledge and skills are required for DevOps that will certainly be helpful in the transition. Here is a step-by-step guide for you: - -- If you do not already know a programming language, learn one. Some languages used in DevOps include [Python](https://roadmap.sh/python) and [Golang](https://roadmap.sh/golang). [Bash](https://www.gnu.org/software/bash/) is commonly used for scripting. -- Learn about file systems and how to use bash to navigate through files. Also, learn to use Command-Line Interfaces (CLIs). -- Learn about [Docker](https://roadmap.sh/docker) and [Kubernetes](https://roadmap.sh/kubernetes). -- Learn about servers and cloud infrastructures. Some of the common cloud service providers include [AWS](https://roadmap.sh/aws), [Azure](https://azure.microsoft.com/), and [GCP](https://cloud.google.com/). - -For more detailed guidance, refer to roadmap.sh's DevOps [beginner](https://roadmap.sh/devops) and [advanced](https://roadmap.sh/devops?r=devops) roadmaps. - -## How to transition to full stack development - -Are you looking to transition into full stack development? Here is a handy guide: - -- Learn HTML, CSS, and JavaScript. -- Learn a JavaScript framework, e.g., [React](https://roadmap.sh/react), [Vue](https://roadmap.sh/vue), [Angular](https://roadmap.sh/angular). -- Learn a backend programming language of your choice, e.g., C#, [Python](https://roadmap.sh/python), [Java](https://roadmap.sh/java) -- Learn a backend framework of your choice, e.g., Node.js, [ASP.NET Core,](https://roadmap.sh/aspnet-core) [Spring boot](https://roadmap.sh/spring-boot). -- Learn database systems i.e SQL and NoSQL databases, e.g., [PostgreSQL](https://roadmap.sh/postgresql-dba), MongoDB - -You can refer to roadmap.sh's [full stack development roadmap](https://roadmap.sh/full-stack) for a more detailed guideline. - -As you've seen, becoming a DevOps engineer and full stack web developer requires several skill sets. Full stack developers and DevOps engineers have important roles in software development but have different areas of expertise and responsibilities. - -Ultimately, the right choice depends on the specific needs and goals of the **software development project**. roadmap.sh offers step-by-step guidance on how to become a [DevOps engineer](https://roadmap.sh/devops?r=devops) and a [full stack developer](https://roadmap.sh/full-stack), and by signing up, you will be able to: - -- Keep track of your progress and also share it on your roadmap.sh profile. -- Collaborate on other official roadmaps. -- Draw your roadmap, either as an individual learner or for [Dev](https://roadmap.sh/teams) [t](https://roadmap.sh/teams)[eams](https://roadmap.sh/teams). -- [Generate new roadmaps](https://roadmap.sh/ai) with AI. diff --git a/src/data/guides/devops-vs-sre.md b/src/data/guides/devops-vs-sre.md deleted file mode 100644 index b10a72b8e..000000000 --- a/src/data/guides/devops-vs-sre.md +++ /dev/null @@ -1,195 +0,0 @@ ---- -title: 'DevOps vs SRE: Key Differences Explained' -description: 'DevOps vs SRE: Discover which role suits your career best. Learn key differences, skills required, and career prospects in our detailed guide.' -authorId: william -excludedBySlug: '/devops/devops-vs-sre' -seo: - title: 'DevOps vs SRE: Key Differences Explained' - description: 'DevOps vs SRE: Discover which role suits your career best. Learn key differences, skills required, and career prospects in our detailed guide.' - ogImageUrl: 'https://assets.roadmap.sh/guest/devops-vs-sre1-pfhzy.jpg' -isNew: false -type: 'textual' -date: 2024-06-13 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![Key differences between DevOps and SRE.](https://assets.roadmap.sh/guest/devops-vs-sre1-pfhzy.jpg) - -When it comes to managing and enhancing software development and operations, companies often adopt sets of practices and processes such as Development Operations (DevOps) or Site Reliability Engineering (SRE). These approaches increase their ability to deliver applications scalably and maintain a fast pace. - -In fact, companies and individuals seeking to deliver applications and services at a higher velocity are increasingly moving away from traditional software development and infrastructure management. They achieve this by either combining selected [DevOps](https://roadmap.sh/devops) and SRE practices or opting for one over the other. - -Despite their shared goal of improving the software delivery process, some nuances set DevOps and SRE apart. - -This guide provides an in-depth discussion of the key differences between each approach, what they entail, similarities, and the [tools](https://roadmap.sh/devops/tools) and technologies involved. Finally, it offers roadmaps for your DevOps or SRE journey. - -## Differences between DevOps and SRE - -The primary difference between DevOps and SRE lies in their primary **goals**. DevOps aims to improve the software development lifecycle (SDLC), while SRE **focuses** on maintaining stability and resilience after application deployment. In addition, they also differ in **scope**, **metrics**, and **team structure**. - -Let’s look at these points in detail: - -![DevOps vs SRE](https://assets.roadmap.sh/guest/the-difference-between-devops-and-sre-85kpt.JPG) - -### Goals - -DevOps engineers' primary focus is to improve the (SDLC) through process automation, enhance collaboration, and promote continuous improvement. Site Reliability Engineers aims to create easily maintained systems by automating processes, manual and repetitive tasks, implementing monitoring and alerting systems, and designing for scale and resilience. - -### Roles and ownership - -In DevOps culture, developers, and other related stakeholders are often involved in operational tasks, sharing ownership of the entire software delivery process from development through to production. - -In contrast, the SRE model promotes a clear separation between development and operations. The development team is focused on building and maintaining features, while the SRE team is responsible for the reliability and availability of services. - -### Scope and mindset - -DevOps is typically applied to self-contained applications or services, encouraging experimentation and innovation with opportunities to learn from failures. In contrast, SRE is applied to entire systems and platforms, primarily focusing on minimizing human error and downtime for the operations teams. - -### Metrics - -In measuring success, DevOps teams track metrics such as the time from code commit to deployment, deployment frequency, failure rate, and overall system performance. On the other hand, Site Reliability Engineers focus on metrics related to service level objectives like Mean Time to Recover (MTTR), Mean Time Between Failures (MTBF), latency, traffic, and the frequency of errors occurring in the system. - -### Team structure and processes - -DevOps teams work more like an Agile development team, ensuring collaboration between development and operations teams. They often break projects into smaller features and use [continuous integration(CI) / continuous delivery (CD)](https://roadmap.sh/guides/ci-cd) to prioritize delivery based on business needs. - -In contrast, SRE teams are highly specialized teams as compared to DevOps. They see the production environment as a highly available service and implement measures to address threats and failures that may arise from deployed features and integrations. - -Both DevOps and SRE aim to enhance the software development process and operations. DevOps primarily focuses on software development and delivery, while SRE concentrates on software operations and maintenance after deployment, emphasizing reliability and scalability strongly. - -## Key components of DevOps - -According to [Statista](https://www.statista.com/statistics/1234098/software-development-process-changes/), DevOps adoption is expected to grow, with **21% of respondents having integrated it into their source code management**. Recruiters are also actively hiring for DevOps job roles, [**with demand currently at 35.5%**](https://www.statista.com/statistics/1296668/top-in-demand-tech-skills-worldwide/)**.** - -One core principle of DevOps is automating manual processes within the SDLC, focusing on reducing costs and minimizing errors. In addition, DevOps embraces continuous integration, enabling companies to adapt and accommodate changes to end-users' needs or business requirements. - -The following are key aspects of DevOps: - -### CI and CD - -One of the fundamental principles of DevOps is to facilitate faster release cycles for software. This is achieved by continuously integrating code into a repository and automatically deploying it to the required environment. - -![ci/cd](https://assets.roadmap.sh/guest/cicd-xrwy2.JPG) - -### Automation - -Development processes such as testing, deployment, and infrastructural provisioning can be tedious, error-prone, and manual. DevOps addresses these challenges through automation. - -![automation](https://assets.roadmap.sh/guest/automation-mdtdr.jpeg) - -### Monitoring - -Proactive monitoring of application performance, health, availability, and customer experience are some of the key principles of DevOps. With this capability, companies can identify issues quickly, iterate on solutions, and continuously improve. - -![metric](https://assets.roadmap.sh/guest/monitoring-uyfmx.jpeg) - -### Collaboration and communication - -The overall goal of shipping secure and reliable software involves stakeholders like the development teams, operations teams, and other relevant teams to adhere to DevOps practices of collaborating and actively communicating throughout the SDLC. - -![collaboration & communication](https://assets.roadmap.sh/guest/collaboration-t3wa1.jpeg) - -### Infrastructure as Code - -Using scripts or declarative definitions to provision and configure infrastructure is a key component in DevOps. This approach enables teams to be consistent, efficiently manage resources, and reproduce environment-specific bugs. - -![iac](https://assets.roadmap.sh/guest/infrastructure-as-code-gj75w.jpeg) - -### Continuous learning and improvement - -In DevOps, teams are encouraged to share knowledge across teams, conduct service failure postmortem, and experiment with new ideas and potential solutions. - -## Key components of SRE - -Site Reliability Engineering can function as a tool, a set of best practices, or a strategy to automate IT operations tasks such as production system management, change management, and incident response. It empowers system administrators to manage large systems using code rather than manual methods. - -The following are the key aspects of SRE: - -### Automation - -One key principle followed by Site Reliability Engineering is establishing a workflow to reduce manual and repetitive work related to operational tasks. SRE ensures that every team member has access to updated automation tools and technologies. - -### Monitoring and alerting - -A core principle of SRE is using real-time monitored metrics and alerts to detect and respond to issues promptly before they impact the development teams and end users. With these systems in place, SRE teams can diagnose and resolve potential issues before they impact the system. - -### Forecast and planning - -Due to user traffic and workload, systems can experience increased demand and heavy resource usage. Site Reliability Engineering implements measures to ensure that such demand is properly handled by forecasting resources and managing related infrastructure. - -### Incident management - -SRE teams define clear processes for detecting, diagnosing, and resolving incidents. When incidents occur, postmortems are conducted to identify root causes and prevent similar issues from recurring. - -### Service Level Objectives (SLOs) - -Site Reliability Engineering aims to deliver higher availability percentages, uptime, and other metrics such as error rates and response time to system customers or users. - -These processes help software developers build and deliver software more efficiently. - -## Similarities between DevOps and SRE -![Similarities](https://assets.roadmap.sh/guest/similarities-devops-sre-eqa7p.jpeg) - -DevOps and SRE principles have become popular and widely adopted by organizations because they create robust and bug-free systems with continuous improvement in mind. Below are some key similarities between DevOps and SRE: - -- Both advocate automating repetitive tasks like continuous testing, deployment, monitoring, etc. -- They promote the use of CI and CD for software releases. -- There is a strong emphasis on real-time monitoring and collection of metrics for diagnosis and performance monitoring. -- Both focus on reducing organizational silos through collaboration and encourage a culture of continuous learning and improvement. -- They prioritize building reliable systems with a great user experience, quick recovery from disruptions, reliability, and effective operations lifecycle management. - -## What is the role of a DevOps engineer? - -[DevOps engineers](https://roadmap.sh/devops) are integral to any organization looking to bridge the gap between development and operations teams. They collaborate closely with software engineers, quality assurance teams, and other stakeholders to achieve this goal. Here are some key responsibilities of a DevOps engineer: - -- Adopts the agile methodology and automation to remove bottlenecks in the SDLC. -- Set up monitoring and logging mechanisms to track the performance, availability, and health of systems. -- Provision resources, deploy, and manage applications on cloud platforms like [AWS](https://roadmap.sh/aws), Azure, Google Cloud, etc. -- Creates standards and manages configuration to enforce and maintain system integrity across multiple environments. -- Creates a plan to optimize system performance and resource utilization. -- Promotes knowledge sharing by carefully documenting processes, procedures, and [best practices](https://roadmap.sh/devops/best-practices). - -To perform these responsibilities, the DevOps team uses many tools to automate and improve their workflow. Here are some of the [DevOps automation tools](https://roadmap.sh/devops/automation-tools) commonly used: - -- **Docker**: [Docker](https://roadmap.sh/docker) is an open-source platform that enables developers to build, deploy, and run containerized applications. -- **Kubernetes**: [Kubernetes](https://roadmap.sh/kubernetes) is an open-source orchestration platform for automating the deployment processes, scaling, and managing containerized applications. -- **Jenkins**: Jenkins is an automation server used for performing CI and CD in a software project. -- **Git**: Git is a distributed version control system for tracking changes in source code during software development. -- **Prometheus**: Prometheus is an open-source application for event monitoring and alerting. -- **Grafana**: Grafana is an open-source analytics and visualization application that helps identify issues and improve the performance of software systems. -- **Ansible**: Ansible is an open-source engine for automating resource provision, configuration management, application deployment, and other IT-related tasks. - -## What is the role of an SRE engineer? - -Similar to the DevOps engineers, SRE engineers are also an integral part of any organization looking to build systems and services that are reliable, available, scalable, and performant. SREs have a narrow focus on specific practices and metrics that allow for improved collaboration and service delivery. Here are some key responsibilities of an SRE engineer: - -- Responsible for maintaining and ensuring system reliability and uptime. -- Collaborate with the development team to design and architect applications. -- Automate tasks by developing tools and scripts for deployment, alerting, and incident response. -- Analyze and plan resources to cater for future growth and scale. -- Creates plans to mitigate or eliminate events or system failures. -- Promotes knowledge sharing by documenting system configuration, procedures, and best practices. - -To perform these responsibilities, the SRE team uses various tools to tackle infrastructure and operational problems. Here are some of the tools commonly used: - -- **Kibana**: Kibana is an open-source data visualization platform for monitoring metrics and events -- **Datadog**: Datadog is a cloud monitoring tool for events, infrastructure hosts, and much more -- **NetApp Cloud Insights**: NetApp Cloud Insights is a tool used to monitor IT infrastructure -- **Terraform**: Terraform is an infrastructure as code tool used to automate infrastructure tasks -- **Ansible**: Ansible is an open-source engine for automating resource provision, configuration management, application deployment, and other IT-related tasks -- **New Relic**: New Relic is a cloud-based full-stack observability platform for monitoring and analyzing metrics -- **Opsgenie**: Opsgenie is an incident response solution with functionalities like on-call scheduling, reporting, analytics, and alerting - -In summary, [DevOps](https://roadmap.sh/devops) focuses on developing and delivering software, and SRE works on the deployed software to ensure it functions as intended and is reliable. They both have similarities and differences that organizations actively adopt when building and maintaining scalable applications. - -Whether you're an experienced developer aiming to improve your skills or a beginner exploring a career as a DevOps or SRE engineer, you might have seen tons of resources available online and maybe get overwhelmed without a clear path on the way to go. Well, not anymore, roadmap.sh provides a comprehensive guide on any of the career paths you choose to follow. You will be able to: - -- Stay updated with a new roadmap, track your progress, and share it on your roadmap.sh profile. -- Learn with like-minded individuals by joining a supportive community when you [sign up](https://roadmap.sh/signup) on roadmap.sh platform. -- [Generate a new roadmap with AI](https://roadmap.sh/ai). diff --git a/src/data/guides/dhcp-in-one-picture.md b/src/data/guides/dhcp-in-one-picture.md deleted file mode 100644 index b32df3362..000000000 --- a/src/data/guides/dhcp-in-one-picture.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: 'DHCP in One Picture' -description: 'Here is what happens when a new device joins the network.' -authorId: 'kamran' -seo: - title: 'DHCP in One Picture - roadmap.sh' - description: 'Here is what happens when a new device joins the network.' -isNew: false -type: 'visual' -date: 2021-04-28 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'visual-guide' - - 'guide-sitemap' ---- - -[![DHCP in One Picture](/guides/dhcp.png)](/guides/dhcp.png) diff --git a/src/data/guides/dns-in-one-picture.md b/src/data/guides/dns-in-one-picture.md deleted file mode 100644 index 8acb42731..000000000 --- a/src/data/guides/dns-in-one-picture.md +++ /dev/null @@ -1,24 +0,0 @@ ---- -title: 'DNS in One Picture' -description: 'Quick illustrative guide on how a website is found on the internet.' -authorId: 'kamran' -seo: - title: 'DNS in One Picture - roadmap.sh' - description: 'Quick illustrative guide on how a website is found on the internet.' -isNew: false -type: 'visual' -date: 2018-12-04 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'visual-guide' - - 'guide-sitemap' ---- - -DNS or Domain Name System is one of the fundamental blocks of the internet. As a developer, you should have at-least the basic understanding of how it works. This article is a brief introduction to what is DNS and how it works. - -DNS at its simplest is like a phonebook on your mobile phone. Whenever you have to call one of your contacts, you can either dial their number from your memory or use their name which will then be used by your mobile phone to search their number in your phone book to call them. Every time you make a new friend, or your existing friend gets a mobile phone, you have to memorize their phone number or save it in your phonebook to be able to call them later on. DNS or Domain Name System, in a similar fashion, is a mechanism that allows you to browse websites on the internet. Just like your mobile phone does not know how to call without knowing the phone number, your browser does not know how to open a website just by the domain name; it needs to know the IP Address for the website to open. You can either type the IP Address to open, or provide the domain name and press enter which will then be used by your browser to find the IP address by going through several hoops. The picture below is the illustration of how your browser finds a website on the internet. - -[![DNS in One Picture](https://i.imgur.com/z9rwm5A.png)](https://i.imgur.com/z9rwm5A.png) diff --git a/src/data/guides/free-resources-to-learn-llms.md b/src/data/guides/free-resources-to-learn-llms.md deleted file mode 100644 index 52219d759..000000000 --- a/src/data/guides/free-resources-to-learn-llms.md +++ /dev/null @@ -1,51 +0,0 @@ ---- -title: '5 Free Resources to Master LLMs' -description: 'Dive into the world of LLMs with these free resources' -authorId: william -seo: - title: '5 Free Resources to Master Language Models (LLMs) - roadmap.sh' - description: 'Looking to dive into the fascinating world of Language Models (LLMs)? Discover the top 5 free resources that will help you learn and excel in understanding LLMs. From comprehensive tutorials to interactive courses, this blog post provides you with the ultimate guide to sharpen your skills and unravel the potential of language models. Start your journey today and become a pro in LLMs without spending a dime!' -isNew: false -type: 'textual' -date: 2023-05-19 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'guide-sitemap' ---- - -Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP), enabling machines to understand and generate human-like text. In my last visual guide, we discussed [what LLMs are and how they work on a high level](https://roadmap.sh/guides/introduction-to-llms). This guide is a curation of 5 free resources to help you further learn about LLMs and NLP. I have personally gone through these and would highly recommend these to anyone wanting to learn about LLMs. - -## NLP Course by HuggingFace - -[huggingface.co/learn/nlp-course](https://huggingface.co/learn/nlp-course/chapter1/1) - -HuggingFace, a leading NLP platform, offers an in-depth NLP course that delves into transformer models, their workings, and how to effectively utilize HuggingFace's models. The course starts with the basics of Datasets and Tokenizers, equipping you with essential knowledge before diving into classic NLP tasks. What sets this course apart is its broader perspective, exploring how transformer models can be applied in speech processing and computer vision domains. By the end, you'll have a strong foundation in using and fine-tuning models from HuggingFace. - -## Prompt Engineering by DeepLearning.AI - -[ChatGPT Prompt Engineering for Developers](https://deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/) - -Prompt engineering is a critical aspect of working with LLMs, and deeplearning.ai offers a comprehensive course focused specifically on this topic. With hands-on practice materials, you'll gain practical knowledge and techniques for effective prompt engineering. By understanding how to craft prompts that yield desired model outputs, you'll enhance the performance and control of LLMs in various applications. This course is a valuable resource for anyone aiming to master the art of prompt engineering. - -## LLM University by Cohere - -[llm.university](https://docs.cohere.com/docs/llmu) - -Cohere's LLM University provides a diverse curriculum covering essential NLP techniques. From semantic search and generation to classification and embeddings, this resource offers comprehensive instruction on a wide range of topics. With a combination of theory and practical exercises, LLM University equips learners with the knowledge and skills necessary to leverage LLMs effectively. Whether you're a beginner or an experienced practitioner, this resource will enhance your understanding and proficiency in various NLP applications. - -## LLMOps - -[LLMOps Course](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/llmops/) - -LLMOps, a dedicated resource for operationalizing LLMs, offers insights into testing, evaluation metrics, deployment, monitoring, and more. This resource takes you beyond the development stage, exploring the crucial aspects of LLMOps in real-world scenarios. Learn how to effectively test LLMs, evaluate their performance, and deploy them in production environments. With an emphasis on test-driven development for LLMs, LLMOps equips you with the necessary knowledge to ensure the reliability and effectiveness of your models. - -## LLM Bootcamp - -[Full Stack LLM Bootcamp](https://fullstackdeeplearning.com/llm-bootcamp/) - -This is a course by a team of UC Berkeley PhD alumni that teaches best practices and tools for building LLM-powered apps. It covers the full stack from prompt engineering to user-centered design. They have a "[Full Stack Deep Learning](https://fullstackdeeplearning.com/course/)" course as well if you are interested in learning that. - -With these 5 free resources, you have a wealth of knowledge at your fingertips to master LLMs and advance your NLP skills. We have also been working on AI related content i.e. including roadmaps and best practices on [roadmap.sh](https://roadmap.sh) so stay tuned for that as well. Happy learning! diff --git a/src/data/guides/frontend-developer-skills.md b/src/data/guides/frontend-developer-skills.md deleted file mode 100644 index 138f44bbc..000000000 --- a/src/data/guides/frontend-developer-skills.md +++ /dev/null @@ -1,264 +0,0 @@ ---- -title: '12 In-Demand Front End Developer Skills to Master' -description: 'Master these 12 in-demand front end developer skills and become a standout candidate in the web development field.' -authorId: fernando -excludedBySlug: '/frontend/developer-skills' -seo: - title: '12 In-Demand Front End Developer Skills to Master' - description: 'Master these 12 in-demand front end developer skills and become a standout candidate in the web development field.' - ogImageUrl: 'https://assets.roadmap.sh/guest/frontend-developer-skills-zdpyd.jpg' -relatedTitle: "Other Guides" -relatedGuidesId: frontend -isNew: false -type: 'textual' -date: 2024-07-04 -sitemap: - priority: 0.7 - changefreq: 'weekly' -tags: - - 'guide' - - 'textual-guide' - - 'guide-sitemap' ---- - -![The best frontend developer skills to learn.](https://assets.roadmap.sh/guest/frontend-developer-skills-zdpyd.jpg) - -Front end development is probably one of the most dynamic fields in our industry, it’s constantly forcing developers to stay up-to-date with the latest trends and technologies. Either by learning about new frameworks or libraries, or simply by making them choose between their current stack and the hot new framework released every other month. - -While working as a front end developer can be headache-inducing sometimes, it’s also a great field for those who love to constantly learn new technologies. Given how active and dynamic the frontend community is. - -In this article, we’ll try to help guide you through the constantly growing front end ecosystem and we’re going to cover 12 essential front end development skills that every front end dev should master to remain competitive and effective in their role. - -## Understanding Front End Development - -But before we move forward, let’s first understand [what front end development is](https://roadmap.sh/frontend). - -Front end development involves creating the parts of a website or application that users interact with directly. This includes designing layouts, implementing visual elements, and ensuring a seamless user experience by coding the interactions through the use of three basic [frontend technologies](https://roadmap.sh/frontend/technologies): HTML, CSS, and JavaScript. - -Unlike backend development, which deals with server-side logic, front end development focuses exclusively on client-side logic. - -With that said, HTML, CSS, and JavaScript aren’t the only three front end developer skills you should be focusing on, in fact, there are many others that are just as important, so let’s take a quick look at them. - -## 1. HTML/CSS/JavaScript - -![html css and javascript layers](https://assets.roadmap.sh/guest/html-css-javascript-layers-7agjb.png) - -HTML, CSS, and JavaScript are the foundational [programming languages for front end development](https://roadmap.sh/frontend/languages). Mind you, they’re not the only skills that matter, but they’re definitely the three skills you should focus on first. - -### HTML (HyperText Markup Language): - -- **Role**: HTML is the backbone of any web page, providing the basic structure and content. It defines elements like headings, paragraphs, links, images, and other multimedia. -- **Key Features**: Semantic HTML5 elements (like `
`, `