[{"data":1,"prerenderedAt":819},["ShallowReactive",2],{"/en-us/blog/beginner-guide-python-programming":3,"navigation-en-us":40,"banner-en-us":451,"footer-en-us":461,"blog-post-authors-en-us-GitLab":700,"blog-related-posts-en-us-beginner-guide-python-programming":714,"blog-promotions-en-us":757,"next-steps-en-us":809},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":20,"description":17,"extension":25,"externalUrl":26,"featured":14,"heroImage":19,"isFeatured":14,"meta":27,"navigation":28,"path":29,"publishedDate":20,"rawbody":30,"seo":31,"slug":13,"stem":35,"tagSlugs":36,"tags":38,"template":15,"updatedDate":26,"__hash__":39},"blogPosts/en-us/blog/beginner-guide-python-programming.yml","How to get started with Python programming",[7],"gitlab",[9],"GitLab","Are you a programming enthusiast who wants to learn Python? Are you new to coding? Do you need help deciding where to begin with Python? If you are looking for answers to these questions, then you are in the right place.\n\n## How to start writing code with Python\n\nPython is an easy-to-learn, easy-to-use and easy-to-deploy programming language, with rampant usage in building web and desktop applications, analyzing data and performing [DevOps](https://about.gitlab.com/topics/devops/) tasks. It is a free, open-source, object-oriented coding language used to write simple scripts and complex programs. Of the almost 700 programming languages, Python is considered one of the best to learn first.\n\n## Installing Python\n\nBefore discussing the basics of Python, it is essential to download and install Python on your desktop/laptop. Python works on multiple platforms, including Linux, Windows and Mac. It comes preinstalled on most Mac and Linux systems; however, you should download the latest version from the official Python website.\n\nTo check the current Python version on your system, open the command line and type “python -V”.\n\n![command prompt](https://about.gitlab.com/images/blogimages/python1.png)\n\nIf you have an outdated version, download either the 32- or 64-bit setup from the website based on your system requirements.\n\nThere are other alternatives for downloading the setup: for Windows, you can install it directly from Microsoft. For Linux, install it using the package manager. For macOS, you can download it from Homebrew.\n\nOnce the setup is downloaded, run the file installer, and click on “Install Now”. Once the installation is complete, you are ready to go. Below is an example of a Python installation for Windows.\n\n![install Python](https://about.gitlab.com/images/blogimages/python2.png)\n\n## Running Python in command prompt\n\nTo verify Python is installed and working correctly in Windows, open the command prompt and enter “python”, which will invoke the interpreter. You can directly execute Python codes in it.  For example, type “2*5+1” and press “enter”. You will see “11” as the output. Entering “quit ()” will exit the interpreter.\n\n![Python interpreter](https://about.gitlab.com/images/blogimages/python3.png)\n\n## Running Python in IDE\n\nWith the latest Python installed, you are now ready to start programming in Python. When writing long scripts or programs in Python, use Python’s built-in Integrated Development and Learning Environment (IDLE).\n\nStart the IDLE and then, from the File dropdown, select “New File”, which opens a new editing window. So now, on your screen, you have two windows: a Python shell and an untitled file.\n\n![Python shell and untitled file](https://about.gitlab.com/images/blogimages/python4.png)\n\nThe Python shell is a REPL environment, which is shorthand for \"read-eval-print loop\". It runs snippets of the code, usually one statement at a time. For example, by repeating the same calculation “2*5+1” that we did in the command prompt, you can see how a Python shell can function as a calculator.\n\n![Python as a calculator](https://about.gitlab.com/images/blogimages/python5.png)\n\nThe untitled window is a text editing window for writing complete programs. The shell displays its output. For example, the conventional first program of Python for beginners is printing “Hello World!”. Make sure you save the text editor before running it by pressing “F5”.\n\n![Hello World](https://about.gitlab.com/images/blogimages/python61.png)\n\n## The basics of Python\n\nWe know you can’t wait to start writing long scripts for games and websites, but you still have a long way to get there. Just like with learning any other language, you must first understand the basics of Python.\n\nThe **print()** function, as seen in the Hello World! example, prints a value on the output window. A value is the most basic thing a program uses. It can be a string, a numeric value or any other Python object. Any object within single/double quotations is called a string. For instance, the “Hello World!” that is printed in the above program is also of the type string. Numeric values like 4 and 4.5 are the types of integers and floats, respectively. You can change an integer or float into a string and vice versa using the built-in functions **int()**, **float()** and **str()**.\n\n![value in an output window](https://about.gitlab.com/images/blogimages/python7.png)\n\n## Python’s vocabulary\n\nPython is the simplest coding language. It is easy to read and understand. Unlike human languages, Python has a small vocabulary or reserved words holding special meaning. Terms other than this reserved vocabulary hold meaning only to you and are called variables. These 35 reserved words are:\n\n![Python terms](https://about.gitlab.com/images/blogimages/python8.png)\n\nMake sure you use these words for their specified purpose to avoid confusing the Python interpreter and causing a syntax error.\n\n### Naming variables\n\nSometimes you want to store values in your code for retrieving them later, which you can do by giving them symbolic names called variables. As seen below, we ask Python to store 5 and 6 with labels x and y, respectively, and then retrieve them later to find their sum.\n\n![storing variables](https://about.gitlab.com/images/blogimages/python9.png)\n\nThere are rules for choosing a name for a variable; failing to follow these gives a syntax error. A few mandatory rules are narrated below:\n\n1. The name can contain both letters and numbers, but it can’t start with a number.\n1. An underscore can appear in the name to separate multiple words.\n1. Special symbols like @#$ are illegal and should not appear in the name.\n1. Python keywords should not be used as names for variables.\n\n### Understanding operators and operands\n\nPython uses special symbols called “operators” for representing basic mathematical computation. The values to which these operators are applied are called operands. The symbols used as operators for subtraction, addition, division, multiplication and exponentiation are  -,+, /, * and **, respectively.\n\n![symbols for operators](https://about.gitlab.com/images/blogimages/python10.png)\n\nThe modulus operator (%) outputs the remainder of the first operand divided by the second operand. It is useful in checking whether a number is divisible by another and extracting the rightmost digit/digits of a number.\n\n![modulus operator](https://about.gitlab.com/images/blogimages/python11.png)\n\n### Using expressions\n\nA combination of values, variables and operators is called an expression. An expression typed in the shell gets evaluated, and the answer is displayed. However, in a script, an expression doesn't do anything on its own.\n\nPython uses the mathematical convention PEMDAS for the operators, which means that P for Parentheses has the highest precedence, then Exponentiation, Multiplication and Division, which have the same priority. Addition and Subtraction come next and also have the same precedence. Operators that have the same preference are also evaluated from left to right.\n\n![PEMDAS](https://about.gitlab.com/images/blogimages/python12.png)\n\nThe Addition and Multiplication operators also work with strings for concatenation and repeating a string, respectively.\n\n![addition and multiplication operators](https://about.gitlab.com/images/blogimages/python13.png)\n\nPython also allows you to take the value for a variable from the user via their keyboard. This can be done using a built-in function called **input**.\n\n![input](https://about.gitlab.com/images/blogimages/python14.png)\n\n## Write your first program\n\nNow it's time to write a short program using everything you've learned here. Write a script that takes two numbers as input and adds them. Do this on your own and see the code below to tally your work.\n\n![write a short program](https://about.gitlab.com/images/blogimages/python15.png)\n\n**Congratulations!** You just wrote your first program.\n\nLearning Python is easy and fun. We just helped you make it through the basics. To become a professional Python Programmer, you still have a lot to learn and practice. Good luck on your journey to becoming an expert coder.\n\nPhoto by \u003Ca href=\"https://unsplash.com/@davidclode?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText\">David Clode\u003C/a> on \u003Ca href=\"https://unsplash.com/s/photos/python?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText\">Unsplash\u003C/a>","devsecops",{"slug":13,"featured":14,"template":15},"beginner-guide-python-programming",false,"BlogPost",{"title":5,"description":17,"authors":18,"heroImage":19,"date":20,"body":10,"category":11,"tags":21},"Python is increasingly popular, and for good reason. Here's our beginner's guide.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664962/Blog/Hero%20Images/python.jpg","2021-10-21",[22,23,24],"DevOps","careers","tutorial","yml",null,{},true,"/en-us/blog/beginner-guide-python-programming","seo:\n  title: How to get started with Python programming\n  description: >-\n    Python is increasingly popular, and for good reason. Here's our beginner's\n    guide.\n  ogTitle: How to get started with Python programming\n  ogDescription: >-\n    Python is increasingly popular, and for good reason. Here's our beginner's\n    guide.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664962/Blog/Hero%20Images/python.jpg\n  ogUrl: https://about.gitlab.com/blog/beginner-guide-python-programming\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: https://about.gitlab.com/blog/beginner-guide-python-programming\ncontent:\n  title: How to get started with Python programming\n  description: >-\n    Python is increasingly popular, and for good reason. Here's our beginner's\n    guide.\n  authors:\n    - GitLab\n  heroImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664962/Blog/Hero%20Images/python.jpg\n  date: '2021-10-21'\n  body: >-\n    Are you a programming enthusiast who wants to learn Python? Are you new to\n    coding? Do you need help deciding where to begin with Python? If you are\n    looking for answers to these questions, then you are in the right place.\n\n\n    ## How to start writing code with Python\n\n\n    Python is an easy-to-learn, easy-to-use and easy-to-deploy programming\n    language, with rampant usage in building web and desktop applications,\n    analyzing data and performing\n    [DevOps](https://about.gitlab.com/topics/devops/) tasks. It is a free,\n    open-source, object-oriented coding language used to write simple scripts\n    and complex programs. Of the almost 700 programming languages, Python is\n    considered one of the best to learn first.\n\n\n    ## Installing Python\n\n\n    Before discussing the basics of Python, it is essential to download and\n    install Python on your desktop/laptop. Python works on multiple platforms,\n    including Linux, Windows and Mac. It comes preinstalled on most Mac and\n    Linux systems; however, you should download the latest version from the\n    official Python website.\n\n\n    To check the current Python version on your system, open the command line\n    and type “python -V”.\n\n\n    ![command prompt](https://about.gitlab.com/images/blogimages/python1.png)\n\n\n    If you have an outdated version, download either the 32- or 64-bit setup\n    from the website based on your system requirements.\n\n\n    There are other alternatives for downloading the setup: for Windows, you can\n    install it directly from Microsoft. For Linux, install it using the package\n    manager. For macOS, you can download it from Homebrew.\n\n\n    Once the setup is downloaded, run the file installer, and click on “Install\n    Now”. Once the installation is complete, you are ready to go. Below is an\n    example of a Python installation for Windows.\n\n\n    ![install Python](https://about.gitlab.com/images/blogimages/python2.png)\n\n\n    ## Running Python in command prompt\n\n\n    To verify Python is installed and working correctly in Windows, open the\n    command prompt and enter “python”, which will invoke the interpreter. You\n    can directly execute Python codes in it.  For example, type “2*5+1” and\n    press “enter”. You will see “11” as the output. Entering “quit ()” will exit\n    the interpreter.\n\n\n    ![Python\n    interpreter](https://about.gitlab.com/images/blogimages/python3.png)\n\n\n    ## Running Python in IDE\n\n\n    With the latest Python installed, you are now ready to start programming in\n    Python. When writing long scripts or programs in Python, use Python’s\n    built-in Integrated Development and Learning Environment (IDLE).\n\n\n    Start the IDLE and then, from the File dropdown, select “New File”, which\n    opens a new editing window. So now, on your screen, you have two windows: a\n    Python shell and an untitled file.\n\n\n    ![Python shell and untitled\n    file](https://about.gitlab.com/images/blogimages/python4.png)\n\n\n    The Python shell is a REPL environment, which is shorthand for\n    \"read-eval-print loop\". It runs snippets of the code, usually one statement\n    at a time. For example, by repeating the same calculation “2*5+1” that we\n    did in the command prompt, you can see how a Python shell can function as a\n    calculator.\n\n\n    ![Python as a\n    calculator](https://about.gitlab.com/images/blogimages/python5.png)\n\n\n    The untitled window is a text editing window for writing complete programs.\n    The shell displays its output. For example, the conventional first program\n    of Python for beginners is printing “Hello World!”. Make sure you save the\n    text editor before running it by pressing “F5”.\n\n\n    ![Hello World](https://about.gitlab.com/images/blogimages/python61.png)\n\n\n    ## The basics of Python\n\n\n    We know you can’t wait to start writing long scripts for games and websites,\n    but you still have a long way to get there. Just like with learning any\n    other language, you must first understand the basics of Python.\n\n\n    The **print()** function, as seen in the Hello World! example, prints a\n    value on the output window. A value is the most basic thing a program uses.\n    It can be a string, a numeric value or any other Python object. Any object\n    within single/double quotations is called a string. For instance, the “Hello\n    World!” that is printed in the above program is also of the type string.\n    Numeric values like 4 and 4.5 are the types of integers and floats,\n    respectively. You can change an integer or float into a string and vice\n    versa using the built-in functions **int()**, **float()** and **str()**.\n\n\n    ![value in an output\n    window](https://about.gitlab.com/images/blogimages/python7.png)\n\n\n    ## Python’s vocabulary\n\n\n    Python is the simplest coding language. It is easy to read and understand.\n    Unlike human languages, Python has a small vocabulary or reserved words\n    holding special meaning. Terms other than this reserved vocabulary hold\n    meaning only to you and are called variables. These 35 reserved words are:\n\n\n    ![Python terms](https://about.gitlab.com/images/blogimages/python8.png)\n\n\n    Make sure you use these words for their specified purpose to avoid confusing\n    the Python interpreter and causing a syntax error.\n\n\n    ### Naming variables\n\n\n    Sometimes you want to store values in your code for retrieving them later,\n    which you can do by giving them symbolic names called variables. As seen\n    below, we ask Python to store 5 and 6 with labels x and y, respectively, and\n    then retrieve them later to find their sum.\n\n\n    ![storing\n    variables](https://about.gitlab.com/images/blogimages/python9.png)\n\n\n    There are rules for choosing a name for a variable; failing to follow these\n    gives a syntax error. A few mandatory rules are narrated below:\n\n\n    1. The name can contain both letters and numbers, but it can’t start with a\n    number.\n\n    1. An underscore can appear in the name to separate multiple words.\n\n    1. Special symbols like @#$ are illegal and should not appear in the name.\n\n    1. Python keywords should not be used as names for variables.\n\n\n    ### Understanding operators and operands\n\n\n    Python uses special symbols called “operators” for representing basic\n    mathematical computation. The values to which these operators are applied\n    are called operands. The symbols used as operators for subtraction,\n    addition, division, multiplication and exponentiation are  -,+, /, * and **,\n    respectively.\n\n\n    ![symbols for\n    operators](https://about.gitlab.com/images/blogimages/python10.png)\n\n\n    The modulus operator (%) outputs the remainder of the first operand divided\n    by the second operand. It is useful in checking whether a number is\n    divisible by another and extracting the rightmost digit/digits of a number.\n\n\n    ![modulus\n    operator](https://about.gitlab.com/images/blogimages/python11.png)\n\n\n    ### Using expressions\n\n\n    A combination of values, variables and operators is called an expression. An\n    expression typed in the shell gets evaluated, and the answer is displayed.\n    However, in a script, an expression doesn't do anything on its own.\n\n\n    Python uses the mathematical convention PEMDAS for the operators, which\n    means that P for Parentheses has the highest precedence, then\n    Exponentiation, Multiplication and Division, which have the same priority.\n    Addition and Subtraction come next and also have the same precedence.\n    Operators that have the same preference are also evaluated from left to\n    right.\n\n\n    ![PEMDAS](https://about.gitlab.com/images/blogimages/python12.png)\n\n\n    The Addition and Multiplication operators also work with strings for\n    concatenation and repeating a string, respectively.\n\n\n    ![addition and multiplication\n    operators](https://about.gitlab.com/images/blogimages/python13.png)\n\n\n    Python also allows you to take the value for a variable from the user via\n    their keyboard. This can be done using a built-in function called **input**.\n\n\n    ![input](https://about.gitlab.com/images/blogimages/python14.png)\n\n\n    ## Write your first program\n\n\n    Now it's time to write a short program using everything you've learned here.\n    Write a script that takes two numbers as input and adds them. Do this on\n    your own and see the code below to tally your work.\n\n\n    ![write a short\n    program](https://about.gitlab.com/images/blogimages/python15.png)\n\n\n    **Congratulations!** You just wrote your first program.\n\n\n    Learning Python is easy and fun. We just helped you make it through the\n    basics. To become a professional Python Programmer, you still have a lot to\n    learn and practice. Good luck on your journey to becoming an expert coder.\n\n\n    Photo by \u003Ca\n    href=\"https://unsplash.com/@davidclode?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText\">David\n    Clode\u003C/a> on \u003Ca\n    href=\"https://unsplash.com/s/photos/python?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText\">Unsplash\u003C/a>\n  category: devsecops\n  tags:\n    - DevOps\n    - careers\n    - tutorial\nconfig:\n  slug: beginner-guide-python-programming\n  featured: false\n  template: 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software development the easy way using GitLab","Learn how University of Washington lecturer Stephen G. Dame uses GitLab for Education to manage student assignments, distribute course materials, and provide inline code feedback at scale.\n",[720],"Rod Burns","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659537/Blog/Hero%20Images/display-article-image-0679-1800x945-fy26.png","2026-04-29","For instructors teaching software development, one of the biggest logistical challenges is assignment distribution and feedback at scale. How do you give large groups of students access to course materials, keep solution code private, and still deliver meaningful, contextual feedback without lots of administrative overhead?\n\nThe **[GitLab for Education program](https://about.gitlab.com/solutions/education/)** provides qualifying institutions with free access to **GitLab Ultimate**, enabling instructors to build professional-grade workflows that mirror real-world software development environments. In this article, you'll learn how Stephen G. Dame, a lecturer in the Computing and Software Systems department at the University of Washington, Bothell, uses simple workflows in GitLab to manage everything from course materials to student feedback across multiple classes.\n\n## From aerospace to academia: Bringing GitLab to the classroom\n\nDame came to academia with years of experience as a chief software engineer at Boeing Commercial Airplanes, where GitLab was used for aerospace projects. As an adjunct professor, he became an early advocate for GitLab within the university, joining the GitLab for Education program to access the full feature set needed to run structured, scalable course workflows.\n\n> **\"GitLab provides the greatest way to organize multiple classes, student assignments, lectures, and code samples through the use of Groups and Subgroups, which I found to be unique to GitLab compared to other repository platforms.\"**\n>\n> - Stephen G. Dame, University of Washington, Bothell\n\n## Set up groups: Build the right structure before writing a line of code\n\nThe foundation of an effective GitLab-based course is a well-planned group hierarchy. GitLab's **[Groups and Subgroups](https://docs.gitlab.com/tutorials/manage_user/#create-the-organization-parent-group-and-subgroups)** allow instructors to model the natural structure of a university department institution, course, and role with precise, inheritable permissions at every level.\n\nDame's structure places the university at the root (`UWTeaching`), with each course occupying its own subgroup (e.g. `css430`). Within each course sit repositories for `lecture-materials` and `code`, alongside dedicated Subgroups for `students` and `graders`. Instructor materials remain private, while student and grader subgroups are configured with controlled permissions so that assignment briefs and solutions are visible only to the right people.\n\n![Screenshot of GitLab group hierarchy — institution, course subgroup, and per-student subgroups](https://res.cloudinary.com/about-gitlab-com/image/upload/v1777463673/dpxfnitv76pdmvcqtgag.png)\n\nPermissions cascade downward through the hierarchy via **Manage > Members**, allowing Dame to add students to a course's `students` subgroup with `Reporter` access and an expiration date tied to the end of the academic quarter. Students can clone and pull from assignment repositories but cannot push — keeping solution code firmly under instructor control.\n\nStudents are guided to set up SSH keys across all their working environments (local machines, cloud shells, virtual machines) so they can clone repositories and receive weekly updates via `git pull`. They copy relevant code into their own private repositories to manage their own version history.\n\n**Tip for large classes:** For larger cohorts, adding students by hand is impractical. GitLab's REST API lets you automate subgroup creation and membership from a list of usernames. Below is a sample Python script that handles this:\n\n```python\n    import gitlab\n    from datetime import datetime\n\n    # Connect to your GitLab instance\n    gl = gitlab.Gitlab('https://gitlab.com', private_token='YOUR_PRIVATE_TOKEN')\n\n    # Target parent group ID (e.g., the ID for \"css430 > students\")\n    parent_group_id = 12345678\n\n    # Set expiration: typically the beginning of the next month after quarter end\n    expiry_date = '2025-01-01'\n\n    # List of collected student usernames\n    student_list = ['alice_css430', 'bob_css430', 'carol_css430', 'dave_css430', 'eve_css430']\n\n    for username in student_list:\n        try:\n            # 1. Create a personal subgroup for the student\n            subgroup = gl.groups.create({\n                'name': username,\n                'path': username,\n                'parent_id': parent_group_id,\n                'visibility': 'private'\n            })\n\n            # 2. Add student to the new subgroup with Expiration\n            user = gl.users.list(username=username)[0]\n            subgroup.members.create({\n                'user_id': user.id,\n                'access_level': gitlab.const.REPORTER_ACCESS,\n                'expires_at': expiry_date\n            })\n            print(f\"Success: Subgroup created and student added for {username}\")\n        except Exception as e:\n            print(f\"Error processing {username}: {e}\")\n```\nThere is also an [open source project that automates class management](https://gitlab.com/edu-docs/class-management-automation) published by GitLab that provides additional tooling for this workflow.\n## Give feedback where the work actually lives\n\nOnce the structure is in place, the feedback workflow is where GitLab's value becomes most apparent to students. Dame asks students to submit assignments by opening a **[merge request](https://docs.gitlab.com/user/project/merge_requests/)** in their repository. This gives instructors an immediate, clean diff of everything the student has written.\n![A GitLab merge request showing inline code comment function for an instructor](https://res.cloudinary.com/about-gitlab-com/image/upload/v1777467468/icclzyglbkwlvfysggbi.png)\nInstructors can click any line of code and leave an **inline comment** — not just flagging what is wrong, but explaining why, and pointing to what to look at next. Students receive this feedback in direct context with their code, which is far more actionable than a comment at the bottom of a submitted document.\n\n## Join GitLab for Education\n\nSetting up your first GitLab assignment takes some initial effort, but once the structure is in place it largely runs itself. The real payoff goes beyond organization: Students graduate having worked daily in an environment that mirrors professional software development, building habits around [version control](https://about.gitlab.com/topics/version-control/) and [code review](https://docs.gitlab.com/development/code_review/) rather than learning them as abstract concepts.\n\nIf you are just getting started, keep it simple. Begin with a single course group, one assignment template, and a basic pipeline. The structure will grow naturally alongside your confidence with the platform.\n\nMake sure to **[sign up for GitLab for Education](https://about.gitlab.com/solutions/education/join/)** so that you and your students can access all top-tier features, including unlimited reviewers on merge requests, additional compute minutes, and expanded storage.\n\n> [Apply to the GitLab for Education program today](https://about.gitlab.com/solutions/education/join/).",[622,725],"open source",{"featured":14,"template":15,"slug":727},"teaching-software-development-the-easy-way-using-gitlab",{"content":729,"config":741},{"description":730,"authors":731,"heroImage":733,"date":734,"title":735,"body":736,"category":11,"tags":737},"AI-generated code is 34% of development work. Discover how to balance productivity gains with quality, reliability, and security.",[732],"Manav Khurana","https://res.cloudinary.com/about-gitlab-com/image/upload/v1767982271/e9ogyosmuummq7j65zqg.png","2026-01-08","AI is reshaping DevSecOps: Attend GitLab Transcend to see what’s next","AI promises a step change in innovation velocity, but most software teams are hitting a wall. According to our latest [Global DevSecOps Report](https://about.gitlab.com/developer-survey/), AI-generated code now accounts for 34% of all development work. Yet 70% of DevSecOps professionals report that AI is making compliance management more difficult, and 76% say agentic AI will create unprecedented security challenges.\n\nThis is the AI paradox: AI accelerates coding, but software delivery slows down as teams struggle to test, secure, and deploy all that code.\n\n## Productivity gains meet workflow bottlenecks\nThe problem isn't AI itself. It's how software gets built today. The traditional DevSecOps lifecycle contains hundreds of small tasks that developers must navigate manually: updating tickets, running tests, requesting reviews, waiting for approvals, fixing merge conflicts, addressing security findings. These tasks drain an average of seven hours per week from every team member, according to our research.\n\nDevelopment teams are producing code faster than ever, but that code still crawls through fragmented toolchains, manual handoffs, and disconnected processes. In fact, 60% of DevSecOps teams use more than five tools for software development overall, and 49% use more than five AI tools. This fragmentation creates collaboration barriers, with 94% of DevSecOps professionals experiencing factors that limit collaboration in the software development lifecycle.\n\nThe answer isn't more tools. It's intelligent orchestration that brings software teams and their AI agents together across projects and release cycles, with enterprise-grade security, governance, and compliance built in.\n\n## Seeking deeper human-AI partnerships\nDevSecOps professionals don't want AI to take over — they want reliable partnerships. The vast majority (82%) say using agentic AI would increase their job satisfaction, and 43% envision an ideal future with a 50/50 split between human and AI contributions. They're ready to trust AI with 37% of their daily tasks without human review, particularly for documentation, test writing, and code reviews.\n\nWhat we heard resoundingly from DevSecOps professionals is that AI won't replace them; rather, it will fundamentally reshape their roles. 83% of DevSecOps professionals believe AI will significantly change their work within five years, and notably, 76% think this will create more engineering jobs, not fewer. As coding becomes easier with AI, engineers who can architect systems, ensure quality, and apply business context will be in high demand.\n\nCritically, 88% agree there are essential human qualities that AI will never fully replace, including creativity, innovation, collaboration, and strategic vision.\n\nSo how can organizations bridge the gap between AI’s promise and the reality of fragmented workflows?\n\n## Join us at GitLab Transcend: Explore how to drive real value with agentic AI\nOn February 10, 2026, GitLab will be hosting Transcend, where we'll reveal how intelligent orchestration transforms AI-powered software development. You'll get a first look at GitLab's upcoming product roadmap and learn how teams are solving real-world challenges by modernizing development workflows with AI.\n\nOrganizations winning in this new era balance AI adoption with security, compliance, and platform consolidation. AI offers genuine productivity gains when implemented thoughtfully — not by replacing human developers, but by freeing DevSecOps professionals to focus on strategic thinking and creative innovation.\n\n[Register for Transcend today](https://about.gitlab.com/events/transcend/virtual/) to secure your spot and discover how intelligent orchestration can help your software teams stay in flow.",[738,739,740],"AI/ML","DevOps platform","security",{"featured":28,"template":15,"slug":742},"ai-is-reshaping-devsecops-attend-gitlab-transcend-to-see-whats-next",{"content":744,"config":755},{"title":745,"description":746,"authors":747,"heroImage":749,"date":750,"body":751,"category":11,"tags":752},"Atlassian ending Data Center as GitLab maintains deployment choice","As Atlassian transitions Data Center customers to cloud-only, GitLab presents a menu of deployment choices that map to business needs.",[748],"Emilio Salvador","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098354/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%281%29_5XrohmuWBNuqL89BxVUzWm_1750098354056.png","2025-10-07","Change is never easy, especially when it's not your choice. Atlassian's announcement that [all Data Center products will reach end-of-life by March 28, 2029](https://www.atlassian.com/blog/announcements/atlassian-ascend), means thousands of organizations must now reconsider their DevSecOps deployment and infrastructure. But you don't have to settle for deployment options that don't fit your needs. GitLab maintains your freedom to choose — whether you need self-managed for compliance, cloud for convenience, or hybrid for flexibility — all within a single AI-powered DevSecOps platform that respects your requirements.\n\nWhile other vendors force migrations to cloud-only architectures, GitLab remains committed to supporting the deployment choices that match your business needs. Whether you're managing sensitive government data, operating in air-gapped environments, or simply prefer the control of self-managed deployments, we understand that one size doesn't fit all.\n\n## The cloud isn't the answer for everyone\n\nFor the many companies that invested millions of dollars in Data Center deployments, including those that migrated to Data Center [after its Server products were discontinued](https://about.gitlab.com/blog/atlassian-server-ending-move-to-a-single-devsecops-platform/), this announcement represents more than a product sunset. It signals a fundamental shift away from customer-centric architecture choices, forcing enterprises into difficult positions: accept a deployment model that doesn't fit their needs, or find a vendor that respects their requirements.\n\nMany of the organizations requiring self-managed deployments represent some of the world's most important organizations: healthcare systems protecting patient data, financial institutions managing trillions in assets, government agencies safeguarding national security, and defense contractors operating in air-gapped environments.\n\nThese organizations don't choose self-managed deployments for convenience; they choose them for compliance, security, and sovereignty requirements that cloud-only architectures simply cannot meet. Organizations operating in closed environments with restricted or no internet access aren't exceptions — they represent a significant portion of enterprise customers across various industries.\n\n![GitLab vs. Atlassian comparison table](https://res.cloudinary.com/about-gitlab-com/image/upload/v1759928476/ynl7wwmkh5xyqhszv46m.jpg)\n\n## The real cost of forced cloud migration goes beyond dollars\n\nWhile cloud-only vendors frame mandatory migrations as \"upgrades,\" organizations face substantial challenges beyond simple financial costs:\n\n* **Lost integration capabilities:** Years of custom integrations with legacy systems, carefully crafted workflows, and enterprise-specific automations become obsolete. Organizations with deep integrations to legacy systems often find cloud migration technically infeasible.\n\n* **Regulatory constraints:** For organizations in regulated industries, cloud migration isn't just complex — it's often not permitted. Data residency requirements, air-gapped environments, and strict regulatory frameworks don't bend to vendor preferences. The absence of single-tenant solutions in many cloud-only approaches creates insurmountable compliance barriers.\n\n* **Productivity impacts:** Cloud-only architectures often require juggling multiple products: separate tools for planning, code management, CI/CD, and documentation. Each tool means another context switch, another integration to maintain, another potential point of failure. GitLab research shows [30% of developers spend at least 50% of their job maintaining and/or integrating their DevSecOps toolchain](https://about.gitlab.com/developer-survey/). Fragmented architectures exacerbate this challenge rather than solving it.\n\n## GitLab offers choice, commitment, and consolidation\n\nEnterprise customers deserve a trustworthy technology partner. That's why we've committed to supporting a range of deployment options — whether you need on-premises for compliance, hybrid for flexibility, or cloud for convenience, the choice remains yours. That commitment continues with [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/), our AI solution that supports developers at every stage of their workflow.\n\nBut we offer more than just deployment flexibility. While other vendors might force you to cobble together their products into a fragmented toolchain, GitLab provides everything in a **comprehensive AI-native DevSecOps platform**. Source code management, CI/CD, security scanning, Agile planning, and documentation are all managed within a single application and a single vendor relationship.\n\nThis isn't theoretical. When Airbus and [Iron Mountain](https://about.gitlab.com/customers/iron-mountain/) evaluated their existing fragmented toolchains, they consistently identified challenges: poor user experience, missing functionalities like built-in security scanning and review apps, and management complexity from plugin troubleshooting. **These aren't minor challenges; they're major blockers for modern software delivery.**\n\n## Your migration path: Simpler than you think\n\nWe've helped thousands of organizations migrate from other vendors, and we've built the tools and expertise to make your transition smooth:\n\n* **Automated migration tools:** Our [Bitbucket Server importer](https://docs.gitlab.com/user/import/bitbucket_server/) brings over repositories, pull requests, comments, and even Large File Storage (LFS) objects. For Jira, our [built-in importer](https://docs.gitlab.com/user/project/import/jira/) handles issues, descriptions, and labels, with professional services available for complex migrations.\n\n* **Proven at scale:** A 500 GiB repository with 13,000 pull requests, 10,000 branches, and 7,000 tags is likely to [take just 8 hours to migrate](https://docs.gitlab.com/user/import/bitbucket_server/) from Bitbucket to GitLab using parallel processing.\n\n* **Immediate ROI:** A [Forrester Consulting Total Economic Impact™ study commissioned by GitLab](https://about.gitlab.com/resources/study-forrester-tei-gitlab-ultimate/) found that investing in GitLab Ultimate confirms these benefits translate to real bottom-line impact, with a three-year 483% ROI, 5x time saved in security related activities, and 25% savings in software toolchain costs.\n\n## Start your journey to a unified DevSecOps platform\n\nForward-thinking organizations aren't waiting for vendor-mandated deadlines. They're evaluating alternatives now, while they have time to migrate thoughtfully to platforms that protect their investments and deliver on promises.\n\nOrganizations invest in self-managed deployments because they need control, compliance, and customization. When vendors deprecate these capabilities, they remove not just features but the fundamental ability to choose environments matching business requirements.\n\nModern DevSecOps platforms should offer complete functionality that respects deployment needs, consolidates toolchains, and accelerates software delivery, without forcing compromises on security or data sovereignty.\n\n[Talk to our sales team](https://about.gitlab.com/sales/) today about your migration options, or explore our [comprehensive migration resources](https://about.gitlab.com/move-to-gitlab-from-atlassian/) to see how thousands of organizations have already made the switch.\n\nYou also can [try GitLab Ultimate with GitLab Duo Enterprise](https://about.gitlab.com/free-trial/devsecops/) for free for 30 days to see what a unified DevSecOps platform can do for your organization.",[573,566,753,754],"product","features",{"featured":28,"template":15,"slug":756},"atlassian-ending-data-center-as-gitlab-maintains-deployment-choice",{"promotions":758},[759,773,784,795],{"id":760,"categories":761,"header":763,"text":764,"button":765,"image":770},"ai-modernization",[762],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":766,"config":767},"Get your AI maturity score",{"href":768,"dataGaName":769,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":771},{"src":772},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":774,"categories":775,"header":776,"text":764,"button":777,"image":781},"devops-modernization",[753,11],"Are you just managing tools or shipping innovation?",{"text":778,"config":779},"Get your DevOps maturity score",{"href":780,"dataGaName":769,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":785,"categories":786,"header":787,"text":764,"button":788,"image":792},"security-modernization",[740],"Are you trading speed for security?",{"text":789,"config":790},"Get your security maturity score",{"href":791,"dataGaName":769,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":793},{"src":794},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":796,"paths":797,"header":800,"text":801,"button":802,"image":807},"github-azure-migration",[798,799],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":803,"config":804},"See how GitLab compares to GitHub",{"href":805,"dataGaName":806,"dataGaLocation":244},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":808},{"src":783},{"header":810,"blurb":811,"button":812,"secondaryButton":817},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":813,"config":814},"Get your free trial",{"href":815,"dataGaName":51,"dataGaLocation":816},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":507,"config":818},{"href":55,"dataGaName":56,"dataGaLocation":816},1777493593356]