[{"data":1,"prerenderedAt":809},["ShallowReactive",2],{"/en-us/blog/three-new-support-tools":3,"navigation-en-us":41,"banner-en-us":441,"footer-en-us":451,"blog-post-authors-en-us-Will Chandler|Sara Kassabian":693,"blog-related-posts-en-us-three-new-support-tools":719,"assessment-promotions-en-us":760,"next-steps-en-us":799},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":28,"isFeatured":13,"meta":29,"navigation":30,"path":31,"publishedDate":22,"seo":32,"stem":36,"tagSlugs":37,"__hash__":40},"blogPosts/en-us/blog/three-new-support-tools.yml","Three New Support Tools",[7,8],"will-chandler","sara-kassabian",null,"engineering",{"slug":12,"featured":13,"template":14},"three-new-support-tools",false,"BlogPost",{"title":16,"description":17,"authors":18,"heroImage":21,"date":22,"body":23,"category":10,"tags":24},"We've open sourced 3 tools to help troubleshoot system performance","Say hello to the open source tools our Support team is using to better summarize customer performance data – and find out how they can help you.",[19,20],"Will Chandler","Sara Kassabian","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749670405/Blog/Hero%20Images/open_source_tools.jpg","2019-07-24","Our self-managed customers often encounter issues related to performance, or the time it takes to execute something. In the past, the [Support team](https://handbook.gitlab.com/handbook/support/) had to pull data from disparate sources and cobble it together in order to analyze performance-related issues.\n\n“We’re dealing with someone else’s computer on support, so we have to be able to handle environments with limited observability,” says [Will Chandler](/company/team/#wchandler), senior support engineer. “We’re at the mercy of their infrastructure. That’s why the team has made tools to reduce the friction.”\n\n“With [GitLab.com](/pricing/), we have all of this fancy tooling that helps us collect performance data,” says [Lee Matos](/company/team/#leematos), support engineering manager. “But when we’re working with customers, we need to be ready to bring lightweight tools that don’t require a lot of setup that we can use based on what they have in place.”\n\nThe Support team is working on becoming more data driven by using three new tools designed to aggregate and summarize performance data for self-managed customers. A focus on data-driven decision-making improves the customer relationship and demonstrates our commitment to making performance a key feature of GitLab.\n\nWe'll look at three open source tools created by GitLab Self-Managed Support. Strace parser is a general tool that could be of use to anyone, while JSON Stats and GitLabSOS are tailored to GitLab, but could be easily modified.\n\n## 1. [Strace parser](https://gitlab.com/gitlab-com/support/toolbox/strace-parser)\n\n[Strace](https://gitlab.com/strace/strace) is a commonly used debugging and diagnostic tool in Linux that captures information about what’s happening inside processes running on our customers’ environments.\n\nUnlike [newer](http://man7.org/linux/man-pages/man1/perf.1.html) and [more powerful](https://github.com/iovisor/bpftrace) tracing tools, strace adds [significant overhead to a process](http://www.brendangregg.com/blog/2014-05-11/strace-wow-much-syscall.html). However, strace is generally available even on very old versions of Linux.\n\nAn strace of a single-threaded program is linear, but following the threads of execution quickly gets difficult when there are many processes being captured. At GitLab Support we are typically tracing [Unicorn](https://bogomips.org/unicorn/) workers or [Gitaly](https://gitlab.com/gitlab-org/gitaly), which are highly concurrent, resulting in hundreds of process IDs being traced and hundreds of thousands of lines of output from traces only a few seconds long.\n\nWill built [strace parser](https://gitlab.com/gitlab-com/support/toolbox/strace-parser) for these types of use cases. Strace parser summarizes the most meaningful processing data delivered by an strace in a more accessible format, allowing users to find the critical section sections of the data quickly.\n\nThe next two examples are from a GitLab customer that was using a very slow file system to host their .gitconfig file, which was a major performance bottleneck. But it was not immediately clear what was happening from the perspective of a user trying to troubleshoot. By running an strace on Gitaly, we were able to get a better understanding of why the system was so slow.\n\n```text\n3694 13:45:06.207369 clock_gettime(CLOCK_MONOTONIC, {3016230, 201254200}) = 0 \u003C0.000015>\n3694 13:45:06.207409 futex(0x7f645bb49664, FUTEX_WAIT_BITSET_PRIVATE, 192398, {3016230, 299906871}, ffffffff \u003Cunfinished ...>\n3542 13:45:06.209616 \u003C... futex resumed> ) = -1 ETIMEDOUT (Connection timed out) \u003C0.005236>\n3542 13:45:06.209639 futex(0x1084ff0, FUTEX_WAKE, 1) = 1 \u003C0.000023>\n3510 13:45:06.209673 \u003C... futex resumed> ) = 0 \u003C0.002909>\n3542 13:45:06.209701 futex(0xc420896548, FUTEX_WAKE, 1 \u003Cunfinished ...>\n3510 13:45:06.209710 pselect6(0, NULL, NULL, NULL, {0, 20000}, NULL \u003Cunfinished ...>\n16780 13:45:06.209740 \u003C... futex resumed> ) = 0 \u003C0.002984>\n3542 13:45:06.209749 \u003C... futex resumed> ) = 1 \u003C0.000043>\n16780 13:45:06.209776 pselect6(0, NULL, NULL, NULL, {0, 3000}, NULL \u003Cunfinished ...>\n3542 13:45:06.209787 futex(0xc420053548, FUTEX_WAKE, 1 \u003Cunfinished ...>\n16780 13:45:06.209839 \u003C... pselect6 resumed> ) = 0 (Timeout) \u003C0.000056>\n3544 13:45:06.209853 \u003C... futex resumed> ) = 0 \u003C0.003148>\n3542 13:45:06.209861 \u003C... futex resumed> ) = 1 \u003C0.000069>\n3510 13:45:06.209868 \u003C... pselect6 resumed> ) = 0 (Timeout) \u003C0.000151>\n3544 13:45:06.209915 epoll_ctl(4\u003Canon_inode:[eventpoll]>, EPOLL_CTL_DEL, 181\u003CUNIX:[164869291]>, 0xc42105bb14 \u003Cunfinished ...>\n16780 13:45:06.210076 write(1\u003Cpipe:[55447]>, \"time=\\\"2019-02-14T18:45:06Z\\\" level=warning msg=\\\"health check failed\\\" error=\\\"rpc error: code = DeadlineExceeded desc = context deadline exceeded\\\" worker.name=gitaly-ruby.4\\n\", 170 \u003Cunfinished ...>\n3544 13:45:06.210093 \u003C... epoll_ctl resumed> ) = 0 \u003C0.000053>\n3542 13:45:06.210101 futex(0x1089020, FUTEX_WAIT, 0, {0, 480025102} \u003Cunfinished ...>\n3510 13:45:06.210109 pselect6(0, NULL, NULL, NULL, {0, 20000}, NULL \u003Cunfinished ...>\n16780 13:45:06.210153 \u003C... write resumed> ) = 170 \u003C0.000064>\n3544 13:45:06.210163 close(181\u003CUNIX:[164869291]> \u003Cunfinished ...>\n```\n\nThis strace delivers more than 300,000 lines about the different Gitaly processes running on this customer’s GitLab environment, making it challenging to decipher the flow of execution.\n\n\n“In this case, we can use strace-parser to say, ‘Just give me all the files that were opened, and sort them by how long it took to open,’” says Will.\n\n```text\n$ strace-parser trace.txt files --sort duration\n\nFiles Opened\n\n pid dur (ms) timestamp error file name\n ------- ---------- --------------- --------------- ---------\n 24670 5203.999 13:45:16.152985 - /efs/gitlab/home/.gitconfig\n 24859 5296.580 13:45:23.367482 - /efs/gitlab/home/.gitconfig\n 24584 5279.810 13:45:09.286019 - /efs/gitlab/home/.gitconfig\n 24666 5276.975 13:45:16.079697 - /efs/gitlab/home/.gitconfig\n 24667 5255.649 13:45:16.101009 - /efs/gitlab/home/.gitconfig\n 14871 2594.364 13:45:18.762347 - /efs/gitlab/home/.gitconfig\n 24885 2440.635 13:45:26.224189 - /efs/gitlab/home/.gitconfig\n 24886 2432.980 13:45:26.231009 - /efs/gitlab/home/.gitconfig\n 24656 55.873 13:45:15.916836 ENOENT /nfs/gitlab/gitdata/repositories/group/project.git/objects/info/alternates\n 24688 42.764 13:45:21.522789 ENOENT /nfs/gitlab/gitdata/repositories/group/project.git/objects/info/alternates\n 3709 39.631 13:45:07.816618 - /efs/gitlab/home/.gitconfig\n 24583 37.959 13:45:09.218283 - /efs/gitlab/home/.gitconfig\n\n```\n\nBy summarizing the data in this way, we see multiple files that took 2-5 seconds to open, which is several orders of magnitude slower than expected.\n\n\n“If it’s a particularly busy server and we’re performing these actions 50 times a second, 100 times a second, that adds up really fast,” says Will. “Strace-Parser lets you drill down quickly, and say, ‘OK, this specific thing we’re doing is super slow.’”\n\n### Get a closer look at processes using strace-parser\n\nStrace-Parser can also be used to drill down into details of a process.\n\nThe previous output showed PID 24670 is one of the slower processes, so we use the parser to understand how this slow call impacted the performance of the process overall.\n\n```text\n$ strace-parser trace.txt pid 24670\n\nPID 24670\n\n 271 syscalls, active time: 5303.438ms, user time: 34.662ms, total time: 5338.100ms\n start time: 13:45:16.116671 end time: 13:45:21.454771\n\n syscall count total (ms) max (ms) avg (ms) min (ms) errors\n ----------------- -------- ---------- ---------- ---------- ---------- --------\n open 29 5223.073 5203.999 180.106 0.031 ENOENT: 9\n read 25 46.303 28.747 1.852 0.031\n access 11 6.948 4.131 0.632 0.056 ENOENT: 3\n lstat 6 5.116 2.130 0.853 0.077 ENOENT: 4\n mmap 32 3.868 0.485 0.121 0.028\n openat 2 3.757 2.934 1.878 0.823\n fstat 28 3.395 0.272 0.121 0.033\n munmap 11 2.551 0.929 0.232 0.056\n rt_sigaction 59 2.548 0.121 0.043 0.024\n close 22 2.375 0.279 0.108 0.032\n mprotect 14 0.927 0.174 0.066 0.032\n execve 1 0.621 0.621 0.621 0.621\n brk 6 0.595 0.210 0.099 0.046\n stat 8 0.388 0.082 0.048 0.027 ENOENT: 3\n getdents 4 0.361 0.138 0.090 0.044\n rt_sigprocmask 3 0.141 0.059 0.047 0.040\n write 1 0.101 0.101 0.101 0.101\n dup2 3 0.090 0.032 0.030 0.026\n arch_prctl 1 0.077 0.077 0.077 0.077\n getrlimit 1 0.062 0.062 0.062 0.062\n getcwd 1 0.044 0.044 0.044 0.044\n set_robust_list 1 0.035 0.035 0.035 0.035\n set_tid_address 1 0.032 0.032 0.032 0.032\n setpgid 1 0.030 0.030 0.030 0.030\n ---------------\n\n Program Executed: /opt/gitlab/embedded/bin/git\n Args: [\"--git-dir\" \"/nfs/gitlab/gitdata/repositories/group/project.git\" \"cat-file\" \"--batch-check\"]\n\n Parent PID: 3563\n\n Slowest file open times for PID 24670:\n\n dur (ms) timestamp error file name\n ---------- --------------- --------------- ---------\n 5203.999 13:45:16.152985 - /efs/gitlab/home/.gitconfig\n 5.420 13:45:16.143520 - /nfs/gitlab/gitdata/repositories/group/project.git/config\n 2.959 13:45:21.372776 - /efs/gitlab/home/.gitconfig\n 2.934 13:45:21.401073 - /nfs/gitlab/gitdata/repositories/group/project.git/refs/\n 2.736 13:45:21.417333 ENOENT /nfs/gitlab/gitdata/repositories/group/project.git/info/grafts\n 2.683 13:45:21.421558 - /nfs/gitlab/gitdata/repositories/group/project.git/objects/b7/ef5eba3a425af1e2a9cf6f51cb87454b6e1ad1\n 2.430 13:45:21.407170 ENOENT /nfs/gitlab/gitdata/repositories/group/project.git/objects/info/alternates\n 0.992 13:45:21.420213 ENOENT /nfs/gitlab/gitdata/repositories/group/project.git/shallow\n 0.823 13:45:21.405535 - /nfs/gitlab/gitdata/repositories/group/project.git/objects/pack\n 0.275 13:45:21.380382 - /nfs/gitlab/gitdata/repositories/group/project.git/config\n\n```\n\nThe output shows the time this process spent working was dominated by the slow file open. This data points the Support team in the right direction for fixing the underlying issue.\n\n\nStrace itself has the `-c` flag which provides a similar summary, but its utility is limited when multiple processes are traced as it cannot break out per-process statistics. Strace-Parser breaks these down to the PID level, and can also include the details of parent and child processes on demand.\n\n“In this case Will has identified an interesting area for our customer and then very quickly anchored it in the fact that when we look at that one spot it was slow,” says Lee. “When we’re debugging, having this data available really helps us pinpoint the problem for our customers so we can give them answers.”\n\nThe typical GitLab deployment has many different processes and services running at a time, which can create dozens of different child processes, so there is a large surface area for potential errors or slowness to occur.\n\nStrace-Parser is an open source, generic tool that anyone can use to better understand their strace data.\n\n## 2. [JSON Stats](https://gitlab.com/gitlab-com/support/toolbox/json_stats)\n\nWill also built [JSON Stats](https://gitlab.com/gitlab-com/support/toolbox/json_stats), a script that pulls performance statistics for different logs from the customer’s GitLab environment and summarizes the results in an easy-to-interpret table.\n\n```text\nMETHOD COUNT RPS PERC99 PERC95 MEDIAN MAX MIN SCORE % FAIL\nFetchRemote 2542 0.17 962176.08 130154.88 36580.23 4988513.00 1940.45 2445851585.19 1.06\nFindAllTags 5200 0.34 30000.37 11538.63 1941.84 30006.23 252.10 156001924.68 1.63\nFindCommit 3506 0.23 20859.98 16622.78 10841.86 30001.59 2528.67 73135073.75 0.23\nFindAllRemoteBranches 1664 0.11 20432.93 12996.75 8606.60 405503.94 1430.84 34000396.10 0.00\nAddRemote 2603 0.17 10001.03 8094.97 825.46 10007.46 228.13 26032673.70 3.00\nFindLocalBranches 2535 0.16 10004.68 10002.90 9051.91 10036.16 1260.89 25361871.05 34.32\n```\n\nThis output shows that we’re calling the “FindLocalBranches” service 2500+ times, and it’s failing 34% of the time.\n\n\nThe Support team can use JSON Stats to ground their findings in evidence when evaluating overall performance for a customer. It's the same concept as strace-parser. Can we pivot the information in a way that it clearly becomes meaningful data?\n\n“It’s a quick way of extracting data that you can give to a customer. Instead of saying ‘Look, this failed once,’ we can say, ‘Look, this is failing a third of the time and that suggests there’s a problem with X,’” says Will.\n\nIn the sample output we see that JSON Stats is working with Gitaly logs, but the tool is nimble enough to work on the logs from all the heavy components of GitLab, including Rails, which runs the UI, and Sidekiq, which works on background tasks.\n\n“Some of our customers are very sophisticated and may have advanced monitoring that could give us this information. But we wanted to build a tool that would help us align and easily standardize on how we can get this performance information for customers that don’t have an advanced monitoring setup,” says Lee.\n\nWhile this specific tool isn't as helpful for people outside of the GitLab community, hopefully it helps to inspire others to consider how they are drawing conclusions, and how they can speed that process up.\n\n### Benchmarking with JSON Stats\n\nWill is building a future iteration of JSON Stats that will compare the performance of a customer’s GitLab instance with GitLab.com.\n\n![JSON benchmarking table](https://about.gitlab.com/images/blogimages/support-tools-update.png){: .shadow}\n\nBenchmarking the performance of GitLab.com (the first row) with the customer environment (second row), and the ratio between the two (third row). We can see that in the worst case, the customer’s 99th percentile FindCommit latency was almost eight times slower than it was on GitLab.com.\n\n\n“Our vision here is to give accountability to our customers. We’re going to treat GitLab.com as the pinnacle experience for GitLab,” says Lee. “We want to use JSON Stats with benchmarking to help us understand how far away our customers are from GitLab.com.”\n\nLee and Will are still assessing how to set the target range for the customer’s instance of GitLab. But considering the wealth of resources allocated to GitLab.com, any self-managed customer that is performing within 5-10% of GitLab.com would be considered hugely successful.\n\n## 3. [GitLab SOS](https://gitlab.com/gitlab-com/support/toolbox/gitlabsos)\n\nWhen a customer encounters an issue, but they are unsure of what they problem is, they can run [GitLab SOS](https://gitlab.com/gitlab-com/support/toolbox/gitlabsos), created by support engineer [Cody West](/company/team/#codyww), to create a snapshot of different activities happening on their system. It's been so helpful in debugging GitLab that it's being added into our [Omnibus delivery](https://gitlab.com/gitlab-org/omnibus-gitlab/merge_requests/3430).\n\nBy capturing so much data about a moment in time during or shortly after encountering a problem, the support team is able to work asynchronously to troubleshoot on behalf of the customer.\n\n```text\ncpuinfo getenforce iotop netstat opt sestatus unicorn_stats\ndf_h gitlab_status lscpu netstat_i pidstat systemctl_unit_files uptime\ndmesg gitlabsos.log meminfo nfsiostat ps tainted var\netc hostname mount nfsstat sar_dev ulimit vmstat\nfree_m iostat mpstat ntpq sar_tcp uname\n```\n\nGitLab SOS works best if the script is run while an issue is occurring, or moments after, but even if the window of opportunity is missed you can still successfully gather information to diagnose the problem.\n\n\n“If a customer is sharp, they may know what problems to look for already,” says Lee. “But if a customer is scared and they don’t know what to look for, then they can lean on a tool like GitLab SOS and learn from GitLab SOS. We even have some sharp customers that will generate the SOS output and begin to troubleshoot themselves because of the comprehensive overview it provides.”\n\n## These new tools drive data-driven decision-making in Support\n\nTools like strace-parser, JSON Stats, and GitLab SOS provide the Support team and GitLab customers with critical evidence about performance. By letting the data drive decision-making, the Support team is able to identify problems faster and quickly start debugging customer environments. Performance is a key feature of GitLab, and by filling our toolbox with data-driven solutions we can ensure greater [transparency](https://handbook.gitlab.com/handbook/values/#transparency) between GitLab and our customers.\n\nLearn more about debugging from a support engineering perspective in a GitLab Unfiltered video.\n\n\u003Cfigure class=\"video_container\">\n \u003Ciframe src=\"https://www.youtube.com/embed/9W6QnpYewik\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\nCover photo by [Diogo Nunes](https://unsplash.com/@dialex?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/search/photos/tools?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)\n",[25,26,27],"open source","features","inside 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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.",[725],"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",[263,615,25],"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":731,"featured":13,"template":14},"how-iit-bombay-students-code-future-with-gitlab",{"content":733,"config":742},{"title":734,"description":735,"authors":736,"heroImage":737,"date":738,"category":10,"tags":739,"body":741},"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.",[725],"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",[615,263,740],"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":743,"featured":30,"template":14},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":745,"config":758},{"category":10,"tags":746,"body":749,"date":750,"updatedDate":751,"heroImage":752,"authors":753,"title":756,"description":757},[747,748,110],"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",[754,755],"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":30,"template":14,"slug":759},"migration-from-azure-devops-to-gitlab",{"promotions":761},[762,776,787],{"id":763,"categories":764,"header":766,"text":767,"button":768,"image":773},"ai-modernization",[765],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":769,"config":770},"Get your AI maturity score",{"href":771,"dataGaName":772,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":774},{"src":775},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":777,"categories":778,"header":779,"text":767,"button":780,"image":784},"devops-modernization",[740,561],"Are you just managing tools or shipping innovation?",{"text":781,"config":782},"Get your DevOps maturity score",{"href":783,"dataGaName":772,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":785},{"src":786},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":788,"categories":789,"header":791,"text":767,"button":792,"image":796},"security-modernization",[790],"security","Are you trading speed for security?",{"text":793,"config":794},"Get your security maturity score",{"href":795,"dataGaName":772,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":797},{"src":798},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":800,"blurb":801,"button":802,"secondaryButton":807},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":803,"config":804},"Get your free trial",{"href":805,"dataGaName":52,"dataGaLocation":806},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":497,"config":808},{"href":56,"dataGaName":57,"dataGaLocation":806},1772652086143]