[{"data":1,"prerenderedAt":795},["ShallowReactive",2],{"/en-us/blog/insights":3,"navigation-en-us":40,"banner-en-us":440,"footer-en-us":450,"blog-post-authors-en-us-Sara Kassabian":690,"blog-related-posts-en-us-insights":704,"assessment-promotions-en-us":746,"next-steps-en-us":785},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":35,"tagSlugs":36,"__hash__":39},"blogPosts/en-us/blog/insights.yml","Insights",[7],"sara-kassabian",null,"engineering",{"slug":11,"featured":12,"template":13},"insights",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"We're dogfooding a tool to help visualize high-level trends in GitLab projects","How our easy to configure Insights technology takes data from issues and merge requests to build visually appealing charts.",[18],"Sara Kassabian","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749681053/Blog/Hero%20Images/birdseyeview.jpg","2020-01-30","Our policy at GitLab is to [dogfood everything](https://handbook.gitlab.com/handbook/engineering/development/principles/#dogfooding) – meaning we aren't going to introduce a new product or feature to our [DevOps platform](/solutions/devops-platform/) before our engineering team tests it out. Sometimes though, the development process happens in reverse: The product and engineering teams need a specific tool or functionality to help us run GitLab better and discover a tool that has the capacity to solve many different customer use cases.\n\n[Insights](https://docs.gitlab.com/ee/user/project/insights/), which is available to [GitLab Ultimate](/pricing/ultimate/) users, is an example of such a tool. Insights is a flexible feature of GitLab that allows our users to visualize different trends in workflows, bugs, merge request (MR) throughput, and issue activity that is based upon the underlying labeling system of a group. In this blog post, we'll go in-depth on how and why we built this tool, how we use the tool at GitLab, and explain how to configure Insights for your own projects.\n\n\n- [Why we built Insights](#why-we-built-insights)\n- [Labels powers Insights](#why-label-hygiene-matters)\n- [How to configure Insights](#configuring-your-insights-dashboard)\n- [How GitLab uses Insights](#how-we-are-dogfooding-insights)\n- [Implementing Insights in your instance](#implementing-insights-for-your-team)\n\n[Kyle Wiebers](/company/team/#kwiebers), quality engineering manager on Engineering Productivity, gives an overview of how we use Insights at GitLab in the GitLab Unfiltered video embedded below. Watch the video and read the rest of the post to learn all about this exciting new tool we're dogfooding at GitLab.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube-nocookie.com/embed/kKnQzS9qorc\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## Why we built Insights\n\nThe [Engineering Productivity team](https://handbook.gitlab.com/handbook/product/product-processes/) at GitLab first built Insights to provide an overview of trends in the issue tracker, but soon realized that this technology can be applied in different ways that were beneficial to our needs, and the needs of our users.\n\n\"The initial thing was we were interested in when the bugs were being raised: Were they being raised around release time or were they being raised the middle of a phase?\" says [Mark Fletcher](/company/team/#markglenfletcher), backend engineer on Engineering Productivity. \"Because we did have bugs being created just after release, which led to regressions, which led to patch fixes. So we were just interested in exploring those kinds of trends.\"\n\nTo capture this trend data the Quality Engineering team created the [quality dashboard](https://quality-dashboard.gitlap.com/groups/gitlab-org), which was essentially the first iteration of Insights for GitLab. While the quality dashboard showed trends in bugs being raised per release cycle, it also showed how much work was being accomplished over the same period.\n\n\"And that's where the scope really changed from looking at issues that are bugs to merge requests and being able to have generic rules based on labels that we can use to align with our workflow,\" says Kyle.\n\n## Why label hygiene matters\n\nThe Engineering Productivity team soon realized that a lot of the different trends they were aiming to capture with Insights were powered by [labels](https://docs.gitlab.com/ee/user/project/labels.html#overview). Labels allow a GitLab user to categorize epics, issues, and merge requests with descriptive titles such as \"bug\" or \"feature request\" and quickly filter based upon category. The label filtering system works inside the [issue tracker](https://gitlab.com/gitlab-org/gitlab/-/issues/?sort=created_date&state=opened&first_page_size=100), and all throughout GitLab, and is a core part of the underlying configuration of Insights.\n\nA good example of an Insights dashboard that is configured by labels and the metadata that underlies issues and merge requests (such as creation date) is the [MR throughputs dashboard](https://gitlab.com/groups/gitlab-org/-/insights/#/throughputs).\n\n![Merge request throughputs for group](https://about.gitlab.com/images/blogimages/merge_throughputs_group.png){: .shadow.medium.center}\nA screenshot of the chart for merge request throughouts at the group level.\n\n\nThe MR throughputs dashboard captures how many MRs are completed during a given week or month to measure our organization's overall performance. It is part of our workflow to assign labels to MRs that help distinguish the type of MR being worked on: feature, bug, community contribution, security, or backstage. This dashboard is configured as a stacked bar chart, which makes it easy to visualize MR throughput by type so we can see the type of work being created over a fixed period of time. The chart is also divided into weekly or monthly views, which helps us see both short- and long-term trends.\n\n\"So, we can look at short-term trends and longer-term trends to see: Are we delivering more work? Are we hitting a bottleneck? Are we plateauing? And that allows us to dive a little bit deeper and take corrective action,\" says Kyle.\n\n### Labels help simplify the configuration of dashboards\n\nIf you look to the lefthand sidebar of the MR throughputs dashboard, you'll notice that the dashboard is configured at the Gitlab-org group level. The group level of GitLab-org contains all of the projects within GitLab-org and therefore captures all of the MR throughput data across all projects.\n\nThe project level is a level below the group level and looks at a specific project contained within a larger group, such as the GitLab project in the GitLab-org group.\n\n![Merge request throughputs for project](https://about.gitlab.com/images/blogimages/mr_throughputs_product.png){: .shadow.medium.center}\nA screenshot of the chart for merge request throughoutputs at the project level.\n\n\nAny Insights dashboard, including the MR throughputs dashboard, can be filtered at the group level or the project level, but the configuration remains the same regardless of how the dashboard is filtered.\n\n\"So everything that's contained within a group, and in our case, it would be the GitLab-org group, you can also have this on a project level,\" says Kyle. \"So if you want to look at Insights on a project, you can configure the same thing on a project. Just for our use case, it made sense to look at MR throughputs across multiple projects versus one specific project.\"\n\nBut in the end, it all comes back to labels. We don't have to configure the Insights dashboard differently for groups and projects because all of our labels at GitLab are set up at the group level and then propagate down to the project level.\n\nOne of the characteristics of Insights that makes it such a valuable feature is that the configuration is so flexible. While most customers will use the same labeling system across groups and projects as GitLab does, it is possible to configure the charts separately at the project and group level.\n\n\"The scope [of Insights] changed from looking at issues that are bugs to merge requests and being able to have generic rules based on labels that we can use to align with our workflow,\" says Kyle. \"Then that flexibility allows any customers to leverage the same feature based on their own specific workflow or labeling practices.\"\n\nA user can use Insights on a group or project regardless of the underlying labeling system. They just need to configure the dashboard according to their workflow.\n\n## Configuring your Insights dashboard\n\nThere are numerous Insights dashboards that are available out of the box or that can be [easily configured](https://docs.gitlab.com/ee/user/project/insights/#configure-your-insights) based on a user's labeling workflow.\n\nAll of the Insights dashboards within GitLab are [driven by a YAML file](https://gitlab.com/gitlab-org/quality/insights-config/-/blob/master/.gitlab/insights.yml). The configuration for each chart includes configuration parameters: title, type, and query.\n\nThe query section defines the type of issues and/or merge requests from the issue tracker that will be included in the chart. The [parameters for which labels are contained in the chart](https://docs.gitlab.com/ee/user/project/insights/#queryfilter_labels) fall under the query section as well.\n\n\"The Insights configuration is actually stored in [one of your project's repositories]. So, it can be changed just like you do any of your code. It can be [version-controlled](/topics/version-control/) so you can see changes over time. That gives you a lot of value to just ensure that there's very clear traceability into why was this dashboard changed, and when was it changed,\" says Kyle.\n\nHere is the configuration that underlies the [MR throughputs dashboard](https://gitlab.com/groups/gitlab-org/-/insights/#/throughputs) we looked at extensively in the section above.\n\n```text\nthroughputs:\n  title: Merge Request Throughputs (product only projects)\n  charts:\n    - title: Throughputs per Week\n      type: stacked-bar\n      query:\n        issuable_type: merge_request\n        issuable_state: merged\n        collection_labels:\n          - Community contribution\n          - security\n          - bug\n          - feature\n          - backstage\n        group_by: week\n        period_limit: 12\n    - title: Throughputs per Month\n      type: stacked-bar\n      query:\n        issuable_type: merge_request\n        issuable_state: merged\n        collection_labels:\n          - Community contribution\n          - security\n          - bug\n          - feature\n          - backstage\n        group_by: month\n        period_limit: 24\n\n```\n\n\nExplore the [Insights YAML file for GitLab](https://gitlab.com/gitlab-org/gitlab-insights/blob/master/.gitlab/insights.yml) to see how we set up some of our other charts.\n\n## How we are dogfooding Insights\n\nInsights is most effective at monitoring high-level trends, as well as measuring performance against a specific measurable objective with the aim of taking corrective action. At GitLab, we've been using our Insights technology in different ways to visualize our overall performance or to answer specific questions.\n\nOur Support and Quality Engineering teams at GitLab currently use Insights, but in different ways. By dogfooding the technology here at GitLab, we've found numerous use cases for Insights that could be valuable to our customers.\n\n### How our Support team uses Insights\n\nThe Support team uses Insights both as an out of the box issue tracking dashboard and as a customized dashboard made possible using automation.\n\n#### Bugs SLO chart\n\nThe [Bugs SLO dashboard](https://gitlab.com/gitlab-org/gitlab/insights/#/bugsPastSLO) was created so the Support department and engineering leaders can identify bugs overdue from SLO.\n\n![Support team Bugs SLO chart](https://about.gitlab.com/images/blogimages/bugs_slo.png){: .shadow.medium.center}\nA chart specially configured for the Support team to show how many bugs missed the SLO each month.\n\n\nThe Bugs SLO chart is configured in the GitLab-org group but lives in the GitLab project. The chart pulls open issues pertaining to bugs and customer bugs, that are labeled `missed-SLO` and groups them by month. We also have a [labeling system for categorizing based on priority](https://docs.gitlab.com/ee/development/labels/index.html#priority-labels) – P1 bugs are top priority, P2 bugs are second priority.\n\n\"This really allows us to, again, look at the trends: Are we improving? Are we getting worse? Do we need to look a little bit deeper here and do a corrective action to help address any problems that we see within the trends that Insights provides?\" says Kyle.\n\n#### Configuration of SLO chart\n\nHere is a peek at what happens inside the YAML file to configure the bugs SLO chart.\n\n```yaml\nbugsPastSLO:\n  title: Bugs Past SLO\n  charts:\n    - title: Open bugs past priority SLO by creation month\n      type: stacked-bar\n      query:\n        issuable_type: issue\n        issuable_state: opened\n        filter_labels:\n          - bug\n          - missed-SLO\n        collection_labels:\n          - P1\n          - P2\n        group_by: month\n        period_limit: 24\n    - title: Open customer bugs past priority SLO by creation month\n      type: stacked-bar\n      query:\n        issuable_type: issue\n        issuable_state: opened\n        filter_labels:\n          - bug\n          - missed-SLO\n          - customer\n        collection_labels:\n          - P1\n          - P2\n        group_by: month\n        period_limit: 24\n\n```\n\n\n#### Triage helps ensure good label hygiene\n\nFor the Bugs SLO chart, we use the [GitLab triage project](https://gitlab.com/gitlab-org/gitlab-triage) to [automatically apply the `missed-SLO` label to open issues with priority labels that miss the SLO target](https://handbook.gitlab.com/handbook/engineering/infrastructure/engineering-productivity/triage-operations/#missed-slo). We use automation here because the GitLab project is so massive, it would not be feasible to manually apply this label based upon the missed SLO target rules. Insights is flexible enough that either manual labeling or automation can be used on any dashboard.\n\n### Support issue tracker\n\nThe Support team used one of our out of the box dashboards to [see how many Support issues are open and closed per month](https://gitlab.com/gitlab-com/support-forum/insights/#/issues) with the [GitLab.com Support Tracker project](https://gitlab.com/gitlab-com/support-forum), which looks at support issues raised by GitLab.com users that don't go through the Support team.\n\n![Support issue tracker](https://about.gitlab.com/images/blogimages/support_issue_tracker.png){: .shadow.medium.center}\nThe Support team also uses one of our out of the box dashboards that tracks the number of issues open and closed each month.\n\n\n\"This shows that [the dashboard] is quite useful out of the box to just see some visualizations without doing any configuration,\" says Mark. \"These were the charts that we thought would give the most value to a team or to a project without doing any config whatsoever.\"\n\n## How our Quality Engineering team uses Insights\n\nThe Quality Engineering team uses Insights to look at opportunities to remedy gaps in a specific project in our EE, as well as to visualize flaky tests on GitLab based on reported issues.\n\n### Enterprise Edition testcases chart\n\nOne of our more specific use cases is the Enterprise testcases chart. The Quality Engineering department is working to close the gap in testcases in the GitLab Enterprise. The team [configured a chart](https://gitlab.com/gitlab-org/quality/testcases/insights/#/eeTestcasesCharts) within the [testcases project](https://gitlab.com/gitlab-org/quality/testcases/tree/master) to help visualize how many open and closed test gaps there are, separated by GitLab product area, and GitLab product tier.\n\n![EE testcases chart](https://about.gitlab.com/images/blogimages/EE_testcases.png){: .shadow.medium.center}\nQuality Engineering configured this chart to visualize gaps in testcases on GitLab Enterprise.\n\n\n\"Looking at this chart, we may say, ‘Maybe we should have a few people focus on the gaps in verify because it has the most open testcases at the current point',\" says Kyle.\n\n#### Configuration of EE testcases chart\n\nThe EE testcases chart is not something that is available out of the box, but the [configuration for the chart](https://gitlab.com/gitlab-org/quality/testcases/blob/master/.gitlab/insights.yml) is pretty simple nonetheless.\n\n```text\neeTestcasesCharts:\n  title: 'Charts for EE Testcases'\n  charts:\n    - title: Open testcases (backlog) by stage\n      type: bar\n      query:\n        issuable_type: issue\n        issuable_state: opened\n        filter_labels:\n          - \"Quality:EE test gaps\"\n        collection_labels:\n          - \"devops::configure\"\n          - \"devops::create\"\n          - \"devops::protect\"\n          - \"devops::enablement\"\n          - \"devops::growth\"\n          - \"devops::manage\"\n          - \"devops::monitor\"\n          - \"devops::package\"\n          - \"devops::plan\"\n          - \"devops::release\"\n          - \"devops::secure\"\n          - \"devops::verify\"\n\n```\n\n\nThe configuration shows that this is a bar chart that is looking at open issues with the filter `Quality:EE test gaps`. The collection labels are what broke the bars out into different columns. While it is possible to illustrate the data in very intricate ways, the underlying schema to configure the chart is actually quite simple, mirroring the process of searching the issue tracker by filtering based on labels.\n\n![Issue tracker](https://about.gitlab.com/images/blogimages/issue_tracker_EE.png){: .shadow.medium.center}\nThe issues represented in the EE testcases chart can be searched for by label using the issue tracker in the testcases project.\n\n\nOpening the issue tracker for the testcases project, you can search by `Quality:EE test gaps` label, select open issues, to see the actual issues represented by the Insights chart.\n\nThe key takeaway: If your team has good label hygiene and a logical workflow, building charts based on Insights should not be particularly challenging.\n\n### End-to-end transient failures\n\nThe Quality Engineering team monitors how often we have reports of flaky tests in our pipeline by looking at the number of issues created that fit the label schema.\n\n![End-to-end transient failure chart](https://about.gitlab.com/images/blogimages/end_to_end_chart.png){: .shadow.medium.center}\nA second chart configured for Quality Engineering is the end-to-end transient failure chart, which looks at flaky tests.\n\n\nSimilar to many of our other charts, this is a stacked bar graph that looks at both open and closed issues on a weekly basis, and the underlying configuration is as you might expect.\n\n```yaml\ntransientFailures:\n  title: End to end transient failures\n  charts:\n    - title: Opened transient failures per week\n      type: stacked-bar\n      query:\n        issuable_type: issue\n        issuable_state: opened\n        filter_labels:\n          - \"Quality\"\n          - \"QA\"\n          - \"bug\"\n        collection_labels:\n          - \"found:gitlab.com\"\n          - \"found:canary.gitlab.com\"\n          - \"found:staging.gitlab.com\"\n          - \"found:staging-orchestrated\"\n          - \"found:dev.gitlab.com\"\n          - \"found:nightly\"\n          - \"found:in MR\"\n        group_by: week\n        period_limit: 24\n    - title: Closed transient failures per week\n      type: stacked-bar\n      query:\n        issuable_type: issue\n        issuable_state: closed\n        filter_labels:\n          - \"Quality\"\n          - \"QA\"\n          - \"bug\"\n        collection_labels:\n          - \"found:gitlab.com\"\n          - \"found:canary.gitlab.com\"\n          - \"found:staging.gitlab.com\"\n          - \"found:staging-orchestrated\"\n          - \"found:dev.gitlab.com\"\n          - \"found:nightly\"\n          - \"found:in MR\"\n        group_by: week\n        period_limit: 24\n\n```\n\n\n## Implementing Insights for your team\n\nIf your team is often pulling data from GitLab through an API or CSV export, and then building charts based on issues and merge request data, then Insights will make your life a lot easier!\n\nSome questions to think about before implementing Insights include: How would you want to categorize the work being done and the issues that are being created? How do you want to monitor the open/close rates on your issues? Also, how do you plan on using labels?\n\nInsights users really need to define their workflows and have a clear idea about how they're using labels. We recommend having some sort of [automated mechanism to ensure good label hygiene](https://handbook.gitlab.com/handbook/engineering/infrastructure/engineering-productivity/triage-operations/#triage-automation). [GitLab Triage](https://gitlab.com/gitlab-org/gitlab-triage) is our open source project that automates labeling of issues on our giant GitLab project and is a good candidate for any organization that has a large backlog of issues.\n\nWe recommend users [read up more on the issues workflow](https://docs.gitlab.com/ee/development/contributing/issue_workflow.html) to learn more about how to use labels and the issue tracker, which is valuable background knowledge to improve your use of Insights.\n\nWe've been dogfooding Insights for a time to help iron out any wrinkles in the implementation or application of this technology, but we also want to hear your ideas of how we can make improvements to Insights. [Create an issue in the GitLab project issue tracker](https://gitlab.com/gitlab-org/gitlab/issues?scope=all&utf8=%E2%9C%93&state=opened&label_name[]=insights) with the Insights label to share your feedback with us!\n\nCover photo by [Aaron Burden](https://unsplash.com/@aaronburden) on [Unsplash](https://unsplash.com/photos/Qy-CBKUg_X8).\n",[23,24,25],"features","DevOps","inside GitLab","yml",{},true,"/en-us/blog/insights",{"title":31,"description":16,"ogTitle":31,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":32,"ogSiteName":33,"ogType":34,"canonicalUrls":32},"GitLab: New Tool to Visualize High-Level Project 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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.",[710],"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",[262,612,714],"open source","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.",[710],"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",[612,262,726],"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":28,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":731,"config":744},{"category":9,"tags":732,"body":735,"date":736,"updatedDate":737,"heroImage":738,"authors":739,"title":742,"description":743},[733,734,109],"tutorial","git","\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",[740,741],"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":28,"template":13,"slug":745},"migration-from-azure-devops-to-gitlab",{"promotions":747},[748,762,773],{"id":749,"categories":750,"header":752,"text":753,"button":754,"image":759},"ai-modernization",[751],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":755,"config":756},"Get your AI maturity score",{"href":757,"dataGaName":758,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":760},{"src":761},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":763,"categories":764,"header":765,"text":753,"button":766,"image":770},"devops-modernization",[726,558],"Are you just managing tools or shipping innovation?",{"text":767,"config":768},"Get your DevOps maturity score",{"href":769,"dataGaName":758,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":771},{"src":772},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":774,"categories":775,"header":777,"text":753,"button":778,"image":782},"security-modernization",[776],"security","Are you trading speed for security?",{"text":779,"config":780},"Get your security maturity score",{"href":781,"dataGaName":758,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":783},{"src":784},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":786,"blurb":787,"button":788,"secondaryButton":793},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":789,"config":790},"Get your free trial",{"href":791,"dataGaName":51,"dataGaLocation":792},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":496,"config":794},{"href":55,"dataGaName":56,"dataGaLocation":792},1772652078319]