[{"data":1,"prerenderedAt":794},["ShallowReactive",2],{"/en-us/blog/rearchitecting-git-object-database-mainentance-for-scale":3,"navigation-en-us":39,"banner-en-us":439,"footer-en-us":449,"blog-post-authors-en-us-Patrick Steinhardt":691,"blog-related-posts-en-us-rearchitecting-git-object-database-mainentance-for-scale":705,"assessment-promotions-en-us":745,"next-steps-en-us":784},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":35,"tagSlugs":36,"__hash__":38},"blogPosts/en-us/blog/rearchitecting-git-object-database-mainentance-for-scale.yml","Rearchitecting Git Object Database Mainentance For Scale",[7],"patrick-steinhardt",null,"engineering",{"slug":11,"featured":12,"template":13},"rearchitecting-git-object-database-mainentance-for-scale",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Why and how we rearchitected Git object database maintenance for scale","Go in-depth into improvements to maintenance of the Git object database for reduced overhead and increased efficiency.",[18],"Patrick Steinhardt","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664413/Blog/Hero%20Images/speedlights.png","2023-11-02","\n[Gitaly](/direction/gitaly/#gitaly-1), the service that is responsible for providing access to Git repositories in GitLab, needs to ensure that the repositories are maintained regularly. Regular maintenance ensures:\n\n- fast access to these repostiories for users\n- reduced resource usage for servers\n\nHowever, repository maintenance is quite expensive by itself and especially so for large monorepos.\n\nIn [a past blog post](/blog/scaling-repository-maintenance/), we discussed how we revamped the foundations of repository maintenance so that we can iterate on the exact maintenance strategy more readily. This blog post will go through improved maintenance strategies for objects hosted in a Git repository, which was enabled by that groundwork.\n\n- [The object database](#the-object-database)\n- [The old way of packing objects](#the-old-way-of-packing-objects)\n- [All-into-one repacks](#all-into-one-repacks)\n- [Deletion of unreachable objects](#deletion-of-unreachable-objects)\n- [Reachability checks](#reachability-checks)\n- [The new way of packing objects](#the-new-way-of-packing-objects)\n- [Cruft packs](#cruft-packs)\n- [More efficient incremental repacks](#more-efficient-incremental-repacks)\n- [Geometric repacking](#geometric-repacking)\n- [Real-world results](#real-world-results)\n\n## The object database\n\nWhenever a user makes changes in a Git repository, these changes come in the form of new objects written into the repository. Typically, any such object is written into the repository as a so-called \"loose object,\" which is a separate file that contains the compressed contents of the object itself with a header that identifies the type of the object.\n\nTo demonstrate this, in the following example we use\n[`git-hash-object(1)`](https://www.git-scm.com/docs/git-hash-object) to write a new blob into the repository:\n\n```shell\n $ git init --bare repository.git\nInitialized empty Git repository in /tmp/repository.git/\n $ cd repository.git/\n $ echo \"contents\" | git hash-object -w --stdin\n12f00e90b6ef79117ce6e650416b8cf517099b78\n $ tree objects\nobjects\n├── 12\n│   └── f00e90b6ef79117ce6e650416b8cf517099b78\n├── info\n└── pack\n\n4 directories, 1 file\n```\n\nAs you can see, the new object was written into the repository and stored as a separate file in the objects database.\n\nOver time, many of these loose objects will accumulate in the repository. Larger repositories tend to have millions of objects, and storing all of them as separate files is going to be inefficient. To ensure that the repository can be served efficiently to our users and to keep the load on servers low, Git will regularly compress loose objects into packfiles. We can compress loose objects manually by using, for example, [`git-pack-objects(1)`](https://www.git-scm.com/docs/git-pack-objects):\n\n```shell\n $ git pack-objects --pack-loose-unreachable ./objects/pack/pack \u003C/dev/null\nEnumerating objects: 1, done.\nCounting objects: 100% (1/1), done.\nWriting objects: 100% (1/1), done.\nTotal 1 (delta 0), reused 0 (delta 0), pack-reused 0\n7ce39d49d7ddbbbbea66ac3d5134e6089210feef\n $ tree objects\n objects/\n├── 12\n│   └── f00e90b6ef79117ce6e650416b8cf517099b78\n├── info\n│   └── packs\n└── pack\n    ├── pack-7ce39d49d7ddbbbbea66ac3d5134e6089210feef.idx\n    └── pack-7ce39d49d7ddbbbbea66ac3d5134e6089210feef.pack\n```\n\nThe loose object was compressed into a packfile (`.pack`) with a packfile index (`.idx`) that is used to efficiently access objects in that packfile.\n\nHowever, the loose object still exists. To remove it, we can execute [`git-prune-packed(1)`](https://www.git-scm.com/docs/git-prune-packed) to delete all objects that have been packed already:\n\n```shell\n $ git prune-packed\n $ tree objects/\nobjects/\n├── info\n│   └── packs\n└── pack\n    ├── pack-7ce39d49d7ddbbbbea66ac3d5134e6089210feef.idx\n    └── pack-7ce39d49d7ddbbbbea66ac3d5134e6089210feef.pack\n```\n\nFor end users of Git, all of this happens automatically because Git calls `git gc --auto` regularly. This command uses heuristics to figure out what needs to be optimized and whether loose objects need to be compressed into packfiles. This command is unsuitable for the server side because:\n\n- The command does not scale well enough in its current form. The Git project must be more conservative about changing defaults because they support a lot of different use cases. Because we know about the specific needs that we have at GitLab, we can adopt new features that allow for more efficient maintenance more readily.\n- The command does not provide an easy way to observe what exactly it is doing, so we cannot provide meaningful metrics.\n- The command does not allow us to fully control all its exact inner workings and so is not flexible enough.\n\nTherefore, Gitaly uses its own maintenance strategy to maintain Git repositories, of which maintaining the object database is one part.\n\n## The old way of packing objects\n\nAny maintenance strategy to pack objects must ensure the following three things to keep a repository efficient and effective with disk space:\n\n- Loose objects must be compressed into packfiles.\n- Packfiles must be merged into larger packfiles.\n- Objects that are not reachable anymore must be deleted eventually.\n\nPrevious to GitLab 16.0, Gitaly used the following three heuristics to ensure that those three things happened:\n\n- If the number of packfiles in the repository exceeds a certain threshold, Gitaly rewrote all packfiles into a single new packfile. Any objects that were unreachable were put into loose files so that they could be deleted after a certain grace period.\n- If the number of loose objects exceeded a certain threshold, Gitaly compressed all reachable loose objects into a new packfile.\n- If the number of loose objects that are older than the grace period for object deletion exceeded a certain threshold, Gitaly deleted those objects.\n\nWhile these heuristics satisfy all three requirements, they have several downsides, especially in large monorepos that contain gigabytes of data.\n\n### All-into-one repacks\n\nFirst and foremost, the first heuristic requires us to do all-into-one repacks where all packfiles are regularly compressed into a single packfile. In Git repositories with high activity levels, we usually create lots of packfiles during normal operations. But because we need to limit the maximum number of packfiles in a repository, we need to regularly do these complete rewrites of all objects.\n\nUnfortunately, doing such an all-into-one repack can be prohibitively expensive in large monorepos. The repacks may allocate large amounts of memory and typically keep multiple CPU cores busy during the repack, which can require hours of time to complete.\n\nSo, ideally, we want to avoid these all-into-one repacks to the best extent possible.\n\n### Deletion of unreachable objects\n\nTo avoid certain race conditions, Gitaly and Git enforce a grace period before an unreachable object is eligible for deletion. This grace period is tracked using the access time of such an unreachable object: If the last access time of the object is earlier than the grace period, the unreachable object can be deleted.\n\nTo track the access time of a single object, the object must exist as a loose object. This means that all objects that are pending deletion will be evictedfrom any packfile they were previously part of and become loose objects.\n\nBecause the grace period we have in place for Gitaly is 14 days, large monorepos tend to grow a large number of such loose object that are pending deletion. This has two effects:\n\n- The number of loose objects overall grows, which makes object lookup less efficient.\n- Loose objects are stored a lot less efficiently than packed objects, which means that the disk space required for the objects that are pending deletion is signficantly higher than if those objects were stored in their packed form.\n\nIdeally, we would be able to store unreachable objects in packed format while still being able to store their last access times separately.\n\n### Reachability checks\n\nCompressing loose objects into a new packfile is done by using an incremental repack. Git will compute the reachability of all objects in the repository and then pack all loose objects that are reachable into a new packfile.\n\nTo determine reachability of an object, we have to perform a complete graph walk. Starting at all objects that are directly referenced, we walk down any links that those objects have to any other objects. Once we reach the root of the object graph, we have then split all objects into two sets, which are the reachable and unreachable objects.\n\nThis operation can be quite expensive and the larger the repository and the more objects it contains, the more expensive this computation gets. As mentioned above though, objects which are about to be deleted need to be stored\nas loose objects such that we can track their last access time. So if our incremental repack compressed all loose objects into a packfile regardless of their reachability, then this would impact our ability to track the grace\nperiod per object.\n\nThe ideal solution here would avoid doing reachability checks altogether while still being able to track the grace period of unreachable objects which are pending deletion individually.\n\n## The new way of packing objects\n\nOver the past two years, the Git project has shipped multiple mechanisms that allow us to address all of these painpoints we had with our old strategy. These new mechanisms come in two different forms:\n\n- Geometric repacking allows us to merge multiple packfiles without having to rewrite all packfiles into one. This feature was introduced in [Git v2.32.0](https://gitlab.com/gitlab-org/git/-/commit/2744383cbda9bbbe4219bd3532757ae6d28460e1).\n- Cruft packs allow us to store objects that are pending deletion in compressed format in a packfile. This feature was introduced in [Git v2.37.0](https://gitlab.com/gitlab-org/git/-/commit/a50036da1a39806a8ae1aba2e2f2fea6f7fb8e08).\n\nThe Gitaly team has reworked the object database maintenance strategy to make use of these new features.\n\n### Cruft packs\nPrevious to Git v2.37.0, pruning objects with a grace period required Git to first unpack packed objects into loose objects. We did this so that we can track the per-object access times for unreachable objects that are pending deletion as explained above. This is inefficient though as it potentially requires us to keep a lot of unreachable objects in loose format until they can be deleted after the grace period.\n\nWith Git v2.37.0, [git-repack(1)](https://www.git-scm.com/docs/git-repack) learned to write [cruft packs](https://git-scm.com/docs/cruft-packs). While a cruft pack looks just like a normal pack, it also has an accompanying\n`.mtimes` file:\n\n```shell\n$ tree objects/\nobjects/\n├── info\n│   └── packs\n└── pack\n    ├── pack-7ce39d49d7ddbbbbea66ac3d5134e6089210feef.idx\n    ├── pack-7ce39d49d7ddbbbbea66ac3d5134e6089210feef.mtimes\n    └── pack-7ce39d49d7ddbbbbea66ac3d5134e6089210feef.pack\n```\n\nThis file contains per-object timestamps that record when the object was last accessed. With this, we can continue to track per-object grace periods while storing the objects in a more efficient way compared to loose objects.\n\nIn Gitaly, we [started to make use of cruft packs](https://gitlab.com/gitlab-org/gitaly/-/merge_requests/5454) in GitLab 15.10 and made the feature generally available in GitLab 15.11. Cruft packs allow us to store objects that are pending deletion more efficiently and with less impact on the overall performance of the repository.\n\n### More efficient incremental repacks\n\nCruft packs also let us fix the issue that we had to do reachability checks when doing incremental repacks.\n\nPreviously, we had to always ensure reachability when packing loose objects so that we don't pack objects that are pending deletion. But now that any such object would be stored as part of a cruft pack and not as a loose pack anymore, we can instead compress all loose files into a packfile. This change was [introduced into Gitaly](https://gitlab.com/gitlab-org/gitaly/-/merge_requests/5660) with GitLab 16.0.\n\nIn an artificial benchmark with the Linux repository, compressing all loose objects into a packfile led to more than a 90-fold speedup, dropping from almost 13 seconds to 174 milliseconds.\n\n### Geometric repacking\n\nLast but not least, we still have the issue that we need to perform regular all-into-one repacks when we have too many packfiles in the repository.\n\nGit v2.32.0 introduced a new \"geometric\" repacking strategy for the [git-repack(1)](https://www.git-scm.com/docs/git-repack) command that will merge multiple packfiles into a single, larger packfile, that we can use to solve this issue.\n\nThis new \"geometric\" strategy tries to ensure that existing packfiles in the repository form a [geometric sequence](https://en.wikipedia.org/wiki/Geometric_progression) where each successive packfile contains at least `n` times as many objects as the preceding packfile. If the sequence isn't maintained, Git will determine a slice of packfiles that it must repack to maintain the sequence again. With this process, we can limit the number of packfiles that exist in the repository without having to repack all objects into a single packfile regularly.\n\nThe following figures demonstrate geometric repacking with a factor of two.\n\n1. We notice that the two smallest packfiles do not form a geometric sequence as they both contain two objects each.\n\n![Geometrically repacking packfiles, initial](https://about.gitlab.com/images/blogimages/2023-10-09-repository-scaling-odb-maintenance/geometric-repacking-1.png)\n\n1. We identify the smallest slice of packfiles that need to be repacked in order to restore the geometric sequence. Merging the smallest two packfiles would lead to a packfile with four objects. This would not be sufficient to restore the geometric sequence as the next-biggest packfile contains four objects, as well.\n\nInstead, we need to merge the smallest three packfiles into a new packfile that contains eight objects in total. As `8 × 2 ≤ 16` the geometric sequence is restored.\n\n![Geometrically repacking packfiles, combining](https://about.gitlab.com/images/blogimages/2023-10-09-repository-scaling-odb-maintenance/geometric-repacking-2.png)\n\n3. We merge those packfiles into a new packfile.\n\n![Geometrically repacking packfiles, final](https://about.gitlab.com/images/blogimages/2023-10-09-repository-scaling-odb-maintenance/geometric-repacking-3.png)\n\nOriginally, we introduced this new feature [into Gitaly in GitLab 15.11](https://gitlab.com/gitlab-org/gitaly/-/merge_requests/5590).\n\nUnfortunately, we had to quickly revert this new mode. It turned out that the geometric strategy was not ready to handle Git repositories that had an alternate object database connected to them. Because we make use of this feature to [deduplicate objects across forks](https://docs.gitlab.com/ee/development/git_object_deduplication.html), the new repacking strategy led to problems.\n\nAs active contributors to the Git project, we set out to fix these limitations in git-repack(1) itself. This led to an [upstream patch series](http://public-inbox.org/git/a07ed50feeec4bfc3e9736bf493b9876896bcdd2.1680606445.git.ps@pks.im/T/#u) that fixed a bunch of limitations around alternate object directories when doing geometric repacks in Git that was then released with Git v2.41.\n\nWith these fixes upstream, we were then able to\n[reintroduce the change](https://gitlab.com/gitlab-org/gitaly/-/merge_requests/5607) and [globally enable our new geometric repacking strategy](https://gitlab.com/gitlab-org/gitaly/-/merge_requests/5745) with GitLab 16.0.\n\n## Real-world results\n\nAll of this is kind of dry and deeply technical. What about the real-world results?\n\nThe following graphs show the global time we spent repacking objects across all projects hosted on GitLab.com.\n\n![Time spent optimizing repositories globally](https://about.gitlab.com/images/blogimages/2023-10-09-repository-scaling-odb-maintenance/global-optimization.png)\n\nThe initial rollout was on April 26 and progressed until April 28. As you can see, there was first a significant increase in repacking time. But after the initial dust settles, we can see that globally the time we spent repacking repositories roughly decreased by almost 20%.\n\nIn the two weeks before we enabled the feature, during weekdays and at peak times we were usually spending around 2.6 days per 12 hours repacking. In the two weeks after the feature was enabled, we spent around 2.12 days per 12 hours\nrepacking objects.\n\nThis is a success by itself already, but the more important question is how it would impact large monorepos, which are significantly harder to keep well-maintained due to their sheer size. Fortunately, the effect of the new housekeeping strategy was a lot more significant here. The following graph shows the time we spent performing housekeeping tasks in our own `gitlab-org` and `gitlab-com` groups, which host some of the most active repositories that have caused issues in the past:\n\n![Time spent optimizing repositories in GitLab groups](https://about.gitlab.com/images/blogimages/2023-10-09-repository-scaling-odb-maintenance/gitlab-groups-optimization.png)\n\nIn summary, we have observed the following improvements:\n\n|                                                        | Before              | After                | Change |\n| ------------------------------------------------------ | ------------------- | -------------------- | ------ |\n| Global accumulated repacking time                      | ~5.2 hours/hour     | ~4.2 hours/hour      | -20%   |\n| Large repositories of gitlab-org and gitlab-com groups | ~0.7-1.0 hours/hour | 0.12-0.15 hours/hour | -80%   |\n\nWe have heard of other customers that saw similar improvements in highly active large monorepositories.\n\n## Manually enable geometric repacking\n\nWhile the new geometric repacking strategy has been default-enabled starting with GitLab 16.0, it was introduced with GitLab 15.11. If you want to use the\nnew geometric repacking mode, you can opt in by setting the\n`gitaly_geometric_repacking` feature flag. 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IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[711],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[261,613,26],"The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":717,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":719,"config":728},{"title":720,"description":721,"authors":722,"heroImage":723,"date":724,"category":9,"tags":725,"body":727},"Artois University elevates research and curriculum with GitLab Ultimate for Education","Artois University's CRIL leveraged the GitLab for Education program to gain free access to Ultimate, transforming advanced research and computer science curricula.",[711],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[613,261,726],"product","Leading academic institutions face a critical challenge: how to provide thousands of students and researchers with industry-standard, **full-featured DevSecOps tools** without compromising institutional control. Many start with basic version control, but the modern curriculum demands integrated capabilities for planning, security, and advanced CI/CD.\n\nThe **GitLab for Education program** is designed to solve this by providing access to **GitLab Ultimate** for qualifying institutions, allowing them to scale their operations and elevate their academic offerings. \n\nThis article showcases a powerful success story from the **Centre de Recherche en Informatique de Lens (CRIL)**, a joint laboratory of **Artois University** and CNRS in France. After years of relying solely on GitLab Community Edition (CE), the university's move to GitLab Ultimate through the GitLab for Education program immediately unlocked advanced capabilities, transforming their teaching, research, and contribution workflows virtually overnight. This story demonstrates why GitLab Ultimate is essential for institutions seeking to deliver advanced computer science and research curricula.\n\n## GitLab Ultimate unlocked: Managing scale and driving academic value\n\n**Artois University's** self-managed GitLab instance is a large-scale operation, supporting nearly **3,000 users** across approximately **19,000 projects**, primarily serving computer science students and researchers. While GitLab Community Edition was robust, the upgrade to GitLab Ultimate provided the sophisticated tooling necessary for managing this scale and facilitating advanced university-level work.\n\n***\"We can see the difference,\" says Daniel Le Berre, head of research at CRIL and the instance maintainer. \"It's a completely different product. Each week reveals new features that directly enhance our productivity and teaching.\"***\n\nThe institution joined the GitLab for Education program specifically because it covers both **instructional and non-commercial research use cases** and offers full access to Ultimate's features, removing significant cost barriers.\n\n### Key GitLab Ultimate benefits for students and researchers\n\n* **Advanced project management at scale:** Master's students now benefit from **GitLab Ultimate's project planning features**. This enables them to structure, track, and manage complex, long-term research projects using professional methodologies like portfolio management and advanced issue tracking that seamlessly roll up across their thousands of projects.\n\n* **Enhanced visibility:** Features like improved dashboards and code previews directly in Markdown files dramatically streamline tracking and documentation review, reducing administrative friction for both instructors and students managing large project loads.\n\n## Comprehensive curriculum: From concepts to continuous delivery\n\nGitLab Ultimate is deeply integrated into the computer science curriculum, moving students beyond simple `git` commands to practical **DevSecOps implementation**.\n\n* **Git fundamentals:** Students begin by visualizing concepts using open-source tools to master Git concepts.\n\n* **Full CI/CD implementation:** Students use GitLab CI for rigorous **Test-Driven Development (TDD)** in their software projects. They learn to build, test, and perform quality assurance using unit and integration testing pipelines—core competency made seamless by the integrated platform.\n\n* **DevSecOps for research and documentation:** The university teaches students that DevSecOps principles are vital for all collaborative work. Inspired by earlier work in Delft, students manage and produce critical research documentation (PDFs from Markdown files) using GitLab, incorporating quality checks like linters and spell checks directly in the CI pipeline. This ensures high-quality, reproducible research output.\n\n* **Future-proofing security skills:** The GitLab Ultimate platform immediately positions the institution to incorporate advanced DevSecOps features like SAST and DAST scanning as their research and development code projects grow, ensuring students are prepared for industry security standards.\n\n## Accelerating open source contributions with GitLab Duo\n\nAccess to the full GitLab platform, including our AI capabilities, has empowered students to make impactful contributions to the wider open source community faster than ever before.\n\nTwo Master's students recently completed direct contributions to the GitLab product, adding the **ORCID identifier** into user profiles. Working on GitLab.com, they leveraged **GitLab Duo's AI chat and code suggestions** to navigate the codebase efficiently.\n\n***\"This would not have been possible without GitLab Duo,\" Daniel Le Berre notes. \"The AI features helped students, who might have lacked deep codebase knowledge, deliver meaningful contributions in just two weeks.\"***\n\nThis demonstrates how providing students with cutting-edge tools **accelerates their learning and impact**, allowing them to translate classroom knowledge into real-world contributions immediately.\n\n## Empowering open research and institutional control\n\nThe stability of the self-managed instance at Artois University is key to its success. This model guarantees **institutional control and stability** — a critical factor for long-term research preservation.\n\nThe institution's expertise in this area was recently highlighted in a major 2024 study led by CRIL, titled: \"[Higher Education and Research Forges in France - Definition, uses, limitations encountered and needs analysis](https://hal.science/hal-04208924v4)\" ([Project on GitLab](https://gitlab.in2p3.fr/coso-college-codes-sources-et-logiciels/forges-esr-en)). The research found that the vast majority of public forges in French Higher Education and Research relied on **GitLab**. This finding underscores the consensus among academic leaders that self-hosted solutions are essential for **data control and longevity**, especially when compared to relying on external, commercial forges.\n\n## Unlock GitLab Ultimate for your institution today\n\nThe success story of **Artois University's CRIL** proves the transformative power of the GitLab for Education program. By providing **free access to GitLab Ultimate**, we enable large-scale institutions to:\n\n1.  **Deliver a modern, integrated DevSecOps curriculum.**\n\n2.  **Support advanced, collaborative research projects with Ultimate planning features.**\n\n3.  **Empower students to make AI-assisted open source contributions.**\n\n4.  **Maintain institutional control and data longevity.**\n\nIf your academic institution is ready to equip its students and researchers with the complete DevSecOps platform and its most advanced features, we invite you to join the program.\n\nThe program provides **free access to GitLab Ultimate** for qualifying instructional and non-commercial research use cases.\n\n**Apply now [online](https://about.gitlab.com/solutions/education/join/).**\n",{"slug":729,"featured":29,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":731,"config":743},{"category":9,"tags":732,"body":734,"date":735,"updatedDate":736,"heroImage":737,"authors":738,"title":741,"description":742},[733,23,108],"tutorial","\nEnterprise teams are increasingly migrating from Azure DevOps to GitLab to gain strategic advantages and accelerate secure software delivery. \n\n\n- GitLab comes with integrated controls, policies, and [compliance frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) that allow organizations to implement software delivery standards at scale. This is especially important for regulated industries.\n\n- [Security testing](https://docs.gitlab.com/user/application_security/) is embedded in the pipeline and results show in the developer workflow, including static application security testing (SAST), source code analysis (SCA), dynamic application security testing (DAST), infrastructure-as-code scanning (IaC), container scanning, and API scanning.\n\n- [AI capabilities](https://about.gitlab.com/gitlab-duo-agent-platform/) across the full software delivery lifecycle include advanced agent orchestration and customizable flows to support how your organizational teams work.\n\n\nGitLab's open-source, open-core approach, flexible deployment options such as single-tenant dedicated and self-managed, and truly unified platform eliminate integration complexity and security gaps. \n\n\nFor teams facing mounting pressure to accelerate delivery while strengthening security posture and maintaining regulatory compliance, GitLab represents not just a migration but a platform evolution.\n\n\nMigrating from Azure DevOps to GitLab can seem like a daunting task, but with the right approach and tools, it can be a smooth and efficient process. This guide will walk you through the steps needed to successfully migrate your projects, repositories, and pipelines from Azure DevOps to GitLab.\n\n\n## Overview\n\nGitLab provides both [Congregate](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/) (maintained by [GitLab Professional Services](https://about.gitlab.com/professional-services/) organization) and [a built-in Git repository import](https://docs.gitlab.com/user/project/import/repo_by_url/) for migrating projects from Azure DevOps (ADO). These options support repository-by-repository or bulk migration and preserve git commit history, branches, and tags. With Congregate and professional services tools, we support additional assets such as wikis, work items, CI/CD variables, container images, packages, pipelines, and more (see this [feature matrix](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/blob/master/customer/ado-migration-features-matrix.md)). Use this guide to plan and execute your migration and complete post-migration follow-up tasks.\n\n\nEnterprises migrating from ADO to GitLab commonly follow a multi-phase approach:\n\n\n- Migrate repositories from ADO to GitLab using Congregate or GitLab's built-in repository migration.\n\n- Migrate pipelines from Azure Pipelines to GitLab CI/CD.\n\n- Migrate remaining assets such as boards, work items, and artifacts to GitLab Issues, Epics, and the Package and Container Registries.\n\n\nHigh-level migration phases:\n\n\n```mermaid\ngraph LR\n    subgraph Prerequisites\n        direction TB\n        A[\"Set up identity provider (IdP) and\u003Cbr/>provision users\"]\n        A --> B[\"Set up runners and\u003Cbr/>third-party integrations\"]\n        B --> I[\"Users enablement and\u003Cbr/>change management\"]\n    end\n    \n    subgraph MigrationPhase[\"Migration phase\"]\n        direction TB\n        C[\"Migrate source code\"]\n        C --> D[\"Preserve contributions and\u003Cbr/> format history\"]\n        D --> E[\"Migrate work items and\u003Cbr/>map to \u003Ca href=\"https://docs.gitlab.com/topics/plan_and_track/\">GitLab Plan \u003Cbr/>and track work\"]\n    end\n    \n    subgraph PostMigration[\"Post-migration steps\"]\n        direction TB\n        F[\"Create or translate \u003Cbr/>ADO pipelines to GitLab CI\"]\n        F --> G[\"Migrate other assets\u003Cbr/>packages and container images\"]\n        G --> H[\"Introduce \u003Ca href=\"https://docs.gitlab.com/user/application_security/secure_your_application/\">security\u003C/a> and\u003Cbr/>SDLC improvements\"]\n    end\n    \n    Prerequisites --> MigrationPhase\n    MigrationPhase --> PostMigration\n\n    style A fill:#FC6D26\n    style B fill:#FC6D26\n    style I fill:#FC6D26\n    style C fill:#8C929D\n    style D fill:#8C929D\n    style E fill:#8C929D\n    style F fill:#FFA500\n    style G fill:#FFA500\n    style H fill:#FFA500\n```\n\n\n## Planning your migration\n\n\n**To plan your migration, ask these questions:**\n\n\n- How soon do we need to complete the migration?\n\n- Do we understand what will be migrated?\n\n- Who will run the migration?\n\n- What organizational structure do we want in GitLab?\n\n- Are there any constraints, limitations, or pitfalls that need to be taken into account?\n\n\nDetermine your timeline, as it will largely dictate your migration approach. Identify champions or groups familiar with both ADO and GitLab platforms (such as early adopters) to help drive adoption and provide guidance.\n\n\n**Inventory what you need to migrate:**\n\n\n- The number of repositories, pull requests, and contributors\n\n- The number and complexity of work items and pipelines\n\n- Repository sizes and dependency relationships\n\n- Critical integrations and runner requirements (agent pools with specific capabilities)\n\n\nUse GitLab Professional Services's [Evaluate](https://gitlab.com/gitlab-org/professional-services-automation/tools/utilities/evaluate#beta-azure-devops) tool to produce a complete inventory of your entire Azure DevOps organization, including repositories, PR counts, contributor lists, number of pipelines, work items, CI/CD variables and more. If you're working with the GitLab Professional Services team, share this report with your engagement manager or technical architect to help plan the migration.\n\n\nMigration timing is primarily driven by pull request count, repository size, and amount of contributions (e.g. comments in PR, work items, etc). For example, 1,000 small repositories with few PRs and limited contributors can migrate much faster than a smaller set of repositories containing tens of thousands of PRs and thousands of contributors. Use your inventory data to estimate effort and plan test runs before proceeding with production migrations.\n\n\nCompare inventory against your desired timeline and decide whether to migrate all repositories at once or in batches. If teams cannot migrate simultaneously, batch and stagger migrations to align with team schedules. For example, in Professional Services engagements, we organize migrations into waves of 200-300 projects to manage complexity and respect API rate limits, both in [GitLab](https://docs.gitlab.com/security/rate_limits/) and [ADO](https://learn.microsoft.com/en-us/azure/devops/integrate/concepts/rate-limits?view=azure-devops).\n\n\nGitLab's built-in [repository importer](https://docs.gitlab.com/user/project/import/repo_by_url/) migrates Git repositories (commits, branches, and tags) one-by-one. Congregate is designed to preserve pull requests (known in GitLab as merge requests), comments, and related metadata where possible; the simple built-in repository import focuses only on the Git data (history, branches, and tags).\n\n\n**Items that typically require separate migration or manual recreation:**\n\n\n- Azure Pipelines - create equivalent GitLab CI/CD pipelines (consult with [CI/CD YAML](https://docs.gitlab.com/ci/yaml/) and/or with [CI/CD components](https://docs.gitlab.com/ci/components/)). Alternatively, consider using AI-based pipeline conversion available in Congregate.\n\n- Work items and boards - map to GitLab Issues, Epics, and Issue Boards.\n\n- Artifacts, container images (ACR) - migrate to GitLab Package Registry or Container Registry.\n\n- Service hooks and external integrations - recreate in GitLab.\n\n- [Permissions models](https://docs.gitlab.com/user/permissions/) differ between ADO and GitLab; review and plan permissions mapping rather than assuming exact preservation.\n\n\nReview what each tool (Congregate vs. built-in import) will migrate and choose the one that fits your needs. Make a list of any data or integrations that must be migrated or recreated manually.\n\n\n**Who will run the migration?**\n\n\nMigrations are typically run by a GitLab group owner or instance administrator, or by a designated migrator who has been granted the necessary permissions on the destination group/project. Congregate and the GitLab import APIs require valid authentication tokens for both Azure DevOps and GitLab.\n\n\n- Decide whether a group owner/admin will perform the migrations or whether you will grant a specific team/person delegated access.\n\n- Ensure the migrator has correctly configured personal access tokens (Azure DevOps and GitLab) with the scopes required by your chosen migration tool (for example, api/read_repository scopes and any tool-specific requirements). \n\n- Test tokens and permissions with a small pilot migration.\n\n**Note:** Congregate leverages file-based import functionality for ADO migrations and requires instance administrator permissions to run ([see our documentation](https://docs.gitlab.com/user/project/settings/import_export/#migrate-projects-by-uploading-an-export-file)). If you are migrating to GitLab.com, consider engaging Professional Services. For more information, see the [Professional Services Full Catalog](https://about.gitlab.com/professional-services/catalog/). Non-admin account cannot preserve contribution attribution!\n\n\n**What organizational structure do we want in GitLab?**\n\nWhile it's possible to map ADO structure directly to GitLab structure, it's recommended to rationalize and simplify the structure during migration. Consider how teams will work in GitLab and design the structure to facilitate collaboration and access management. Here is a way to think about mapping ADO structure to GitLab structure:\n\n\n```mermaid\ngraph TD\n    subgraph GitLab\n        direction TB\n        A[\"Top-level Group\"]\n        B[\"Subgroup (optional)\"]\n        C[\"Projects\"]\n        A --> B\n        A --> C\n        B --> C\n    end\n\n    subgraph AzureDevOps[\"Azure DevOps\"]\n        direction TB\n        F[\"Organizations\"]\n        G[\"Projects\"]\n        H[\"Repositories\"]\n        F --> G\n        G --> H\n    end\n\n    style A fill:#FC6D26\n    style B fill:#FC6D26\n    style C fill:#FC6D26\n    style F fill:#8C929D\n    style G fill:#8C929D\n    style H fill:#8C929D\n```\n\nRecommended approach:\n\n\n- Map each ADO organization to a GitLab group (or a small set of groups), not to many small groups. Avoid creating a GitLab group for every ADO team project. Use migration as an opportunity to rationalize your GitLab structure.\n\n- Use subgroups and project-level permissions to group related repositories.\n\n- Manage access to sets of projects by using GitLab groups and group membership (groups and subgroups) rather than one group per team project.\n\n- Review GitLab [permissions](https://docs.gitlab.com/ee/user/permissions.html) and consider [SAML Group Links](https://docs.gitlab.com/user/group/saml_sso/group_sync/) to implement an enterprise RBAC model for your GitLab instance (or a GitLab.com namespace).\n\n\n**ADO Boards and work items: State of migration**\n\n\nIt's important to understand how work items migrate from ADO into GitLab Plan (issues, epics, and boards).\n\n\n- ADO Boards and work items map to GitLab Issues, Epics, and Issue Boards. Plan how your workflows and board configurations will translate.\n\n- ADO Epics and Features become GitLab Epics.\n\n- Other work item types (e.g., user stories, tasks, bugs) become project-scoped issues.\n\n- Most standard fields are preserved; selected custom fields can be migrated when supported.\n\n- Parent-child relationships are retained so Epics reference all related issues.\n\n- Links to pull requests are converted to merge request links to maintain development traceability.\n\n\nExample: Migration of an individual work item to a GitLab Issue, including field accuracy and relationships:\n\n\n![Example: Migration of an individual work item to a GitLab Issue](https://res.cloudinary.com/about-gitlab-com/image/upload/v1764769188/ztesjnxxfbwmfmtckyga.png)\n\n\nBatching guidance:\n\n\n- If you need to run migrations in batches, use your new group/subgroup structure to define batches (for example, by ADO organization or by product area).\n\n- Use inventory reports to drive batch selection and test each batch with a pilot migration before scaling.\n\n\n**Pipelines migration**\n\n\nCongregate [recently introduced](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/merge_requests/1298) AI-powered conversion for multi-stage YAML pipelines from Azure DevOps to GitLab CI/CD. This automated conversion works best for simple, single-file pipelines and is designed to provide a working starting point rather than a production-ready `.gitlab-ci.yml` file. The tool generates a functionally equivalent GitLab pipeline that you can then refine and optimize for your specific needs.\n\n\n- Converts Azure Pipelines YAML to `.gitlab-ci.yml` format automatically.\n\n- Best suited for straightforward, single-file pipeline configurations.\n\n- Provides a boilerplate to accelerate migration, not a final production artifact.\n\n- Requires review and adjustment for complex scenarios, custom tasks, or enterprise requirements.\n\n- Does not support Azure DevOps classic release pipelines — [convert these to multi-stage YAML](https://learn.microsoft.com/en-us/azure/devops/pipelines/release/from-classic-pipelines?view=azure-devops) first.\n\n\nRepository owners should review the [GitLab CI/CD documentation](https://docs.gitlab.com/ci/) to further optimize and enhance their pipelines after the initial conversion.\n\n\nExample of converted pipelines:\n\n\n```yml \n\n# azure-pipelines.yml\n\ntrigger:\n  - main\n\nvariables:\n  imageName: myapp\n\nstages:\n  - stage: Build\n    jobs:\n      - job: Build\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          - task: Docker@2\n            displayName: Build Docker image\n            inputs:\n              command: build\n              repository: $(imageName)\n              Dockerfile: '**/Dockerfile'\n              tags: |\n                $(Build.BuildId)\n\n  - stage: Test\n    jobs:\n      - job: Test\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          # Example: run tests inside the container\n          - script: |\n              docker run --rm $(imageName):$(Build.BuildId) npm test\n            displayName: Run tests\n\n  - stage: Push\n    jobs:\n      - job: Push\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          - task: Docker@2\n            displayName: Login to ACR\n            inputs:\n              command: login\n              containerRegistry: '\u003Cyour-acr-service-connection>'\n\n          - task: Docker@2\n            displayName: Push image to ACR\n            inputs:\n              command: push\n              repository: $(imageName)\n              tags: |\n                $(Build.BuildId)\n\n```\n\n```yaml\n\n# .gitlab-ci.yml\n\nvariables:\n  imageName: myapp\n\nstages:\n  - build\n  - test\n  - push\n\nbuild:\n  stage: build\n  image: docker:latest\n  services:\n    - docker:dind\n  script:\n    - docker build -t $imageName:$CI_PIPELINE_ID -f $(find . -name Dockerfile) .\n  only:\n    - main\n\ntest:\n  stage: test\n  image: docker:latest\n  services:\n    - docker:dind\n  script:\n    - docker run --rm $imageName:$CI_PIPELINE_ID npm test\n  only:\n    - main\n\npush:\n  stage: push\n  image: docker:latest\n  services:\n    - docker:dind\n  before_script:\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n  script:\n    - docker tag $imageName:$CI_PIPELINE_ID $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n    - docker push $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n  only:\n    - main\n\n```\n\n**Final checklist:**\n\n\n- Decide timeline and batch strategy.\n\n- Produce a full inventory of repositories, PRs, and contributors.\n\n- Choose Congregate or the built-in import based on scope (PRs and metadata vs. Git data only).\n\n- Decide who will run migrations and ensure tokens/permissions are configured.\n\n- Identify assets that must be migrated separately (pipelines, work items, artifacts, and hooks) and plan those efforts.\n\n- Run pilot migrations, validate results, then scale according to your plan.\n\n\n## Running your migrations\n\n\nAfter planning, execute migrations in stages, starting with trial runs. Trial migrations help surface org-specific issues early and let you measure duration, validate outcomes, and fine-tune your approach before production.\n\n\nWhat trial migrations validate:\n\n\n- Whether a given repository and related assets migrate successfully (history, branches, tags; plus MRs/comments if using Congregate)\n\n- Whether the destination is usable immediately (permissions, runners, CI/CD variables, integrations)\n\n- How long each batch takes, to set schedules and stakeholder expectations\n\n\nDowntime guidance:\n\n\n- GitLab's built-in Git import and Congregate do not inherently require downtime.\n\n- For production waves, freeze changes in ADO (branch protections or read-only) to avoid missed commits, PR updates, or work items created mid-migration.\n\n- Trial runs do not require freezes and can be run anytime.\n\n\nBatching guidance:\n\n\n- Run trial batches back-to-back to shorten elapsed time; let teams validate results asynchronously.\n\n- Use your planned group/subgroup structure to define batches and respect API rate limits.\n\n\nRecommended steps:\n\n\n1. Create a test destination in GitLab for trials:\n\n\n  - GitLab.com: create a dedicated group/namespace (for example, my-org-sandbox)\n\n  - Self-managed: create a top-level group or a separate test instance if needed\n\n\n2. Prepare authentication:\n\n\n  - Azure DevOps PAT with required scopes.\n\n  - GitLab Personal Access Token with api and read_repository (plus admin access for file-based imports used by Congregate).\n\n\n3. Run trial migrations:\n\n\n  - Repos only: use GitLab's built-in import (Repo by URL)\n\n  - Repos + PRs/MRs and additional assets: use Congregate\n\n\n4. Post-trial follow-up:\n\n\n  - Verify repo history, branches, tags; merge requests (if migrated), issues/epics (if migrated), labels, and relationships.\n\n  - Check permissions/roles, protected branches, required approvals, runners/tags, variables/secrets, integrations/webhooks.\n\n  - Validate pipelines (`.gitlab-ci.yml`) or converted pipelines where applicable.\n\n\n5. Ask users to validate functionality and data fidelity.\n\n6. Resolve issues uncovered during trials and update your runbooks.\n\n7. Network and security:\n\n\n  - If your destination uses IP allow lists, add the IPs of your migration host and any required runners/integrations so imports can succeed.\n\n\n8. Run production migrations in waves:\n\n\n  - Enforce change freezes in ADO during each wave.\n\n  - Monitor progress and logs; retry or adjust batch sizes if you hit rate limits.\n\n\n9. Optional: remove the sandbox group or archive it after you finish.\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/ibIXGfrVbi4?si=ZxOVnXjCF-h4Ne0N\" frameborder=\"0\" allowfullscreen=\"true\">\u003C/iframe>\n\u003C/figure>\n\n\n## Terminology reference for GitLab and Azure DevOps\n\n| GitLab                                                           | Azure DevOps                                 | Similarities & Key Differences                                                                                                                                          |\n| ---------------------------------------------------------------- | -------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Group                                                            | Organization                                 | Top-level namespace, membership, policies. ADO org contains Projects; GitLab Group contains Subgroups and Projects.                                                   |\n| Group or Subgroup                                                | Project                                      | Logical container, permissions boundary. ADO Project holds many repos; GitLab Groups/Subgroups organize many Projects.                                                |\n| Project (includes a Git repo)                                    | Repository (inside a Project)                | Git history, branches, tags. In GitLab, a \"Project\" is the repo plus issues, CI/CD, wiki, etc. One repo per Project.                                                  |\n| Merge Request (MR)                                               | Pull Request (PR)                            | Code review, discussions, approvals. MR rules include approvals, required pipelines, code owners.                                                                     |\n| Protected Branches, MR Approval Rules, Status Checks             | Branch Policies                              | Enforce reviews and checks. GitLab combines protections + approval rules + required status checks.                                                                    |\n| GitLab CI/CD                                                     | Azure Pipelines                              | YAML pipelines, stages/jobs, logs. ADO also has classic UI pipelines; GitLab centers on .gitlab-ci.yml.                                                               |\n| .gitlab-ci.yml                                                   | azure-pipelines.yml                          | Defines stages/jobs/triggers. Syntax/features differ; map jobs, variables, artifacts, and triggers.                                                                   |\n| Runners (shared/specific)                                        | Agents / Agent Pools                         | Execute jobs on machines/containers. Target via demands (ADO) vs tags (GitLab). Registration/scoping differs.                                                         |\n| CI/CD Variables (project/group/instance), Protected/Masked       | Pipeline Variables, Variable Groups, Library | Pass config/secrets to jobs. GitLab supports group inheritance and masking/protection flags.                                                                          |\n| Integrations, CI/CD Variables, Deploy Keys                       | Service Connections                          | External auth to services/clouds. Map to integrations or variables; cloud-specific helpers available.                                                                 |\n| Environments & Deployments (protected envs)                      | Environments (with approvals)                | Track deploy targets/history. Approvals via protected envs and manual jobs in GitLab.                                                                                 |\n| Releases (tag + notes)                                           | Releases (classic or pipelines)              | Versioned notes/artifacts. GitLab Release ties to tags; deployments tracked separately.                                                                               |\n| Job Artifacts                                                    | Pipeline Artifacts                           | Persist job outputs. Retention/expiry configured per job or project.                                                                                                  |\n| Package Registry (NuGet/npm/Maven/PyPI/Composer, etc.)           | Azure Artifacts (NuGet/npm/Maven, etc.)      | Package hosting. Auth/namespace differ; migrate per package type.                                                                                                     |\n| GitLab Container Registry                                        | Azure Container Registry (ACR) or others     | OCI images. GitLab provides per-project/group registries.                                                                                                             |\n| Issue Boards                                                     | Boards                                       | Visualize work by columns. GitLab boards are label-driven; multiple boards per project/group.                                                                         |\n| Issues (types/labels), Epics                                     | Work Items (User Story/Bug/Task)             | Track units of work. Map ADO types/fields to labels/custom fields; epics at group level.                                                                              |\n| Epics, Parent/Child Issues                                       | Epics/Features                               | Hierarchy of work. Schema differs; use epics + issue relationships.                                                                                                   |\n| Milestones and Iterations                                        | Iteration Paths                              | Time-boxing. GitLab Iterations (group feature) or Milestones per project/group.                                                                                       |\n| Labels (scoped labels)                                           | Area Paths                                   | Categorization/ownership. Replace hierarchical areas with scoped labels.                                                                                              |\n| Project/Group Wiki                                               | Project Wiki                                 | Markdown wiki. Backed by repos in both; layout/auth differ slightly.                                                                                                  |\n| Test reports via CI, Requirements/Test Management, integrations  | Test Plans/Cases/Runs                        | QA evidence/traceability. No 1:1 with ADO Test Plans; often use CI reports + issues/requirements.                                                                     |\n| Roles (Owner/Maintainer/Developer/Reporter/Guest) + custom roles | Access levels + granular permissions         | Control read/write/admin. Models differ; leverage group inheritance and protected resources.                                                                          |\n| Webhooks                                                         | Service Hooks                                | Event-driven integrations. Event names/payloads differ; reconfigure endpoints.                                                                                        |\n| Advanced Search                                                  | Code Search                                  | Full-text repo search. Self-managed GitLab may need Elasticsearch/OpenSearch for advanced features.                                                                   |\n","2025-12-03","2026-01-16","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749658924/Blog/Hero%20Images/securitylifecycle-light.png",[739,740],"Evgeny Rudinsky","Michael Leopard","Guide: Migrate from Azure DevOps to GitLab","Learn how to carry out the full migration from Azure DevOps to GitLab using GitLab Professional Services migration tools — from planning and execution to post-migration follow-up tasks.",{"featured":29,"template":13,"slug":744},"migration-from-azure-devops-to-gitlab",{"promotions":746},[747,761,772],{"id":748,"categories":749,"header":751,"text":752,"button":753,"image":758},"ai-modernization",[750],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":754,"config":755},"Get your AI maturity score",{"href":756,"dataGaName":757,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":759},{"src":760},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":762,"categories":763,"header":764,"text":752,"button":765,"image":769},"devops-modernization",[726,559],"Are you just managing tools or shipping innovation?",{"text":766,"config":767},"Get your DevOps maturity score",{"href":768,"dataGaName":757,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":773,"categories":774,"header":776,"text":752,"button":777,"image":781},"security-modernization",[775],"security","Are you trading speed for security?",{"text":778,"config":779},"Get your security maturity score",{"href":780,"dataGaName":757,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":785,"blurb":786,"button":787,"secondaryButton":792},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":788,"config":789},"Get your free trial",{"href":790,"dataGaName":50,"dataGaLocation":791},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":495,"config":793},{"href":54,"dataGaName":55,"dataGaLocation":791},1772652097928]