[{"data":1,"prerenderedAt":796},["ShallowReactive",2],{"/en-us/blog/multi-account-aws-sam-deployments-with-gitlab-ci":3,"navigation-en-us":43,"banner-en-us":441,"footer-en-us":451,"blog-post-authors-en-us-Forrest Brazeal":692,"blog-related-posts-en-us-multi-account-aws-sam-deployments-with-gitlab-ci":706,"assessment-promotions-en-us":747,"next-steps-en-us":786},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":29,"isFeatured":12,"meta":30,"navigation":31,"path":32,"publishedDate":20,"seo":33,"stem":37,"tagSlugs":38,"__hash__":42},"blogPosts/en-us/blog/multi-account-aws-sam-deployments-with-gitlab-ci.yml","Multi Account Aws Sam Deployments With Gitlab Ci",[7],"forrest-brazeal",null,"engineering",{"slug":11,"featured":12,"template":13},"multi-account-aws-sam-deployments-with-gitlab-ci",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How to set up multi-account AWS SAM deployments with GitLab CI/CD","Our guest author, an AWS Serverless hero, shares how to automate SAM deployments using GitLab CI/CD.",[18],"Forrest Brazeal","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749666959/Blog/Hero%20Images/gitlab-aws-cover.png","2019-02-04","I've been working with [serverless](/topics/serverless/) applications in AWS for about three years – that makes me an old salt in serverless terms! So I know that deploying and maintaining a serverless app can be tricky; the tooling often has critical gaps.\n\nAWS's [SAM (Serverless Application Model)](https://aws.amazon.com/serverless/sam/) is an open source framework that makes it easier to define AWS resources – such as Lambda functions, API Gateway APIs and DynamoDB tables – commonly used in serverless applications. Once you lay out your app in a SAM template, the next thing you need is a consistent, repeatable way to get that template off your laptop and deployed in the cloud.\n\nYou need CI/CD.\n\nI've used several different [CI/CD systems](/topics/ci-cd/) to automate SAM deployments, and I always look for the following features:\n\n- A single deployment pipeline that can build once and securely deploy to multiple AWS accounts (dev, staging, prod).\n- Dynamic feature branch deployments, so serverless devs can collaborate in the cloud without stepping on each other.\n- Automated cleanup of feature deployments.\n- Review of our SAM application directly integrated with the CI/CD tool's user interface.\n- Manual confirmation before code is released into production.\n\nIn this post, we'll find out how [GitLab CI](/solutions/continuous-integration/) can check these boxes on its way to delivering effective CI/CD for AWS SAM. You can follow along using [the official example code, available here](https://gitlab.com/gitlab-examples/aws-sam).\n\n## Multi-account AWS deployments\n\nWe'll want to set up our deployment pipeline across multiple AWS accounts, because accounts are the only true security boundary in AWS. We don't want to run any risk of deploying prod data in dev, or vice versa. Our multi-account setup will look something like this:\n\nAny time we work with multiple AWS accounts, we need cross-account [IAM roles](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles.html) in order to authorize deployments. We'll handle this task through the following steps. (All referenced scripts are available in the [example repo](https://gitlab.com/gitlab-examples/aws-sam))\n\n### 1\\. Establish three AWS accounts for development, staging, and production deployments\n\nYou can use existing AWS accounts if you have them, or [provision new ones under an AWS Organization](https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_accounts_create.html).\n\n### 2\\. Set up GitLab IAM roles in each account\n\nRun the following AWS CLI call with admin credentials in each of the three accounts:\n\n```shell\naws cloudformation deploy --stack-name GitLabCIRoles --template-file setup-templates/roles.yml --capabilities CAPABILITY_NAMED_IAM --parameter-overrides CIAccountID=\"\u003CAWS Account ID where your GitLab CI/CD runner lives>\" CIAccountSTSCondition=\"\u003CThe aws:userid for the IAM principal used by the Gitlab runner>\"\n ```\n\nReplace `CIAccountID` and `CIAccountSTSCondition` as indicated with values from the AWS account where your GitLab CI/CD runner exists. (Need help finding the `aws:userid` for your runner’s IAM principal? Check out [this guide](https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_variables.html#principaltable).)\n\nThis CloudFormation template defines two roles: `SharedServiceRole` and `SharedDeploymentRole`. The `SharedServiceRole` is assumed by the GitLab CI/CD runner when calling the AWS CloudFormation service. This role trusts the GitLab CI/CD runner's role. It has permissions to call the CloudFormation service, pass a role via IAM, and access S3 and CloudFront: nothing else. This role is not privileged enough to do arbitrary AWS deployments on its own.\n\nThe `SharedDeploymentRole`, on the other hand, has full administrative access to perform any AWS action. A such, it cannot be assumed directly by the GitLab CI/CD runner. Instead, this role must be \"passed\" to CloudFormation using the service's `RoleArn` parameter. The CloudFormation service trusts the `SharedDeploymentRole` and can use it to deploy whatever resources are needed as part of the pipeline.\n\n### 3\\. Create an S3 bucket for CI artifacts\n\nGrab the AWS account ID for each of your development, staging, and production accounts, then deploy this CloudFormation template **in the account where your GitLab CI/CD Runner exists**:\n\n`aws cloudformation deploy --stack-name GitLabCIBucket --template-file setup-templates/ci-bucket.yml --parameter-overrides DevAwsAccountId=\"\u003CAWS Account ID for dev>\" StagingAwsAccountId=\"\u003CAWS Account ID for staging>\" ProdAwsAccountId=\"\u003CAWS Account ID for prod>\" ArtifactBucketName=\"\u003CA unique name for your bucket>\"`\n\nThis CloudFormation template creates a centralized S3 bucket which holds the artifacts created during your pipeline run. Artifacts are created once for each branch push and reused between staging and production. The bucket policy allows the development, test, and production accounts to reference the same artifacts when deploying CloudFormation stacks -- checking off our \"build once, deploy many\" requirement.\n\n### 4\\. Assume the `SharedServiceRole` before making any cross-account AWS calls\nWe have provided the script `assume-role.sh`, which will assume the provided role and export temporary AWS credentials to the current shell. It is sourced in the various `.gitlab-ci.yml` build scripts.\n\n## Single deployment pipeline\n\nThat brings us to the `.gitlab-ci.yml` file you can see at the root of our example repository. GitLab CI/CD is smart enough to dynamically create and execute the pipeline based on that template when we push code to GitLab. The file has a number of variables at the top that you can tweak based on your environment specifics.\n\n### Stages\n\nOur Gitlab CI/CD pipeline contains seven possible stages, defined as follows:\n\n![Multi-account AWS SAM deployment model with GitLab CI](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/deployment-model.png){: .shadow.medium.center}\n\n```yaml\nstages:\n - test\n - build-dev\n - deploy-dev\n - build-staging\n - deploy-staging\n - create-change-prod\n - execute-change-prod\n\n```\n\n![Deployment lifecycle stages](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/deployment-lifecycle-stages.png){: .shadow.medium.center}\n\n\"Stages\" are used as a control flow mechanism when building the pipeline. Multiple build jobs within a stage will run in parallel, but all jobs in a given stage must complete before any jobs belonging to the next stage in the list can be executed.\n\nAlthough seven stages are defined here, only certain ones will execute, depending on what kind of Git action triggered our pipeline. We effectively have three stages to any deployment: a \"test\" phase where we run unit tests and dependency scans against our code, a \"build\" phase that packages our SAM template, and a \"deploy\" phase split into two parts: creating a [CloudFormation change set](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-cfn-updating-stacks-changesets.html) and then executing that change set in the target environment.\n\n#### Test\n\nOur `.gitlab-ci.yml` file currently runs two types of tests: unit tests against our code, and dependency scans against our third-party Python packages.\n\n##### Unit tests\n\nUnit tests run on every branch pushed to the remote repository. This behavior is defined by the `only: branches` property in the job shown below:\n\n```yaml\ntest:unit:\n stage: test\n only:\n - branches\n script: |\n if test -f requirements.txt; then\n pip install -r requirements.txt\n fi\n python -m pytest --ignore=functions/\n\n```\n\nEvery GitLab CI/CD job runs a script. Here, we install any dependencies, then execute Python unit tests.\n\n##### Dependency scans\n\n[Dependency scans](https://docs.gitlab.com/ee/user/application_security/dependency_scanning/), which can take a few minutes, run only on code pushed to the master branch; it would be counterproductive for developers to wait on them every time they want to test code.\n\nThese scans use a hardcoded, standard Docker image to mount the code and run \"Docker in Docker\" checks against a database of known package vulnerabilities. If a vulnerability is found, the pipeline will log the error without stopping the build (that's what the `allow-failure: true` property does).\n\n#### Build\n\nThe build stage turns our SAM template into CloudFormation and turns our Python code into a valid AWS Lambda deployment package. For example, here's the `build:dev` job:\n\n```yaml\nbuild:dev:\n stage: build-dev\n \u003C\u003C: *build_script\n variables:\n \u003C\u003C: *dev_variables\n artifacts:\n paths:\n - deployment.yml\n expire_in: 1 week\n only:\n - branches\n except:\n - master\n\n```\n\nWhat's going on here? Note first the combination of `only` and `except` properties to ensure that our development builds happen only on pushes to branches that aren't `master`. We're referring to `dev_variables`, the set of development-specific variables defined at the top of `.gitlab-ci.yml`. And we're running a script, pointed to by `build_script`, which packages our SAM template and code for deployment using the `aws cloudformation package` CLI call.\n\nThe artifact `deployment.yml` is the CloudFormation template output by our package command. It has all the implicit SAM magic expanded into CloudFormation resources. By managing it as an artifact, we can pass it along to further steps in the build pipeline, even though it isn't committed to our repository.\n\n#### Deploy\nOur deployments use AWS CloudFormation to deploy the packaged application in a target AWS environment.\n\nIn development and staging environments, we use the `aws cloudformation deploy` command to create a change set and immediately execute it. In production, we put a manual \"wait\" in the pipeline at this point so you have the opportunity to review the change set before moving onto the \"Execute\" step, which actually calls `aws cloudformation execute-changeset` to update the underlying stack.\n\nOur deployment jobs use a helper script, committed to the top level of the example repository, called `cfn-wait.sh`. This script is needed because the `aws cloudformation` commands don't wait for results; they report success as soon as the stack operation starts. To properly record the deployment results in our job, we need a script that polls the CloudFormation service and throws an error if the deployment or update fails.\n\n## Dynamic feature branch deployments and Review Apps\n\n![Dynamic feature branch deployments and Review Apps](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/dynamic-feature-branch-deployments.png){: .shadow.medium.center}\n\nWhen a non-master branch is pushed to GitLab, our pipeline runs tests, builds the [updated source code](/solutions/source-code-management/), and deploys and/or updates the changed CloudFormation resources in the development AWS account. When the branch is merged into master, or if someone clicks the \"Stop\" button next to the branch's environment in GitLab CI, the CloudFormation stack will be torn down automatically.\n\nIt is perfectly possible, and indeed desirable, to have multiple development feature branches simultaneously deployed as live environments for more efficient parallel feature development and QA. The serverless model makes this a cost-effective strategy for collaborating in the cloud.\n\nIf we are dynamically deploying our application on every branch push, we might like to view it as part of our interaction with the GitLab console (such as during a code review). GitLab supports this with a nifty feature called [Review Apps](https://docs.gitlab.com/ee/ci/review_apps/). Review Apps allow you to specify an \"environment\" as part of a deployment job, as seen in our `deploy:dev` job below:\n\n```yaml\ndeploy:dev:\n \u003C\u003C: *deploy_script\n stage: deploy-dev\n dependencies:\n - build:dev\n variables:\n \u003C\u003C: *dev_variables\n environment:\n name: review/$CI_COMMIT_REF_NAME\n url: https://${CI_COMMIT_REF_NAME}.${DEV_HOSTED_ZONE_NAME}/services\n on_stop: stop:dev\n only:\n - branches\n except:\n - master\n\n```\n\nThe link specified in the `url` field of the `environment` property will be accessible in the `Environments` section of GitLab CI/CD or on any merge request of the associated branch. (In the case of the sample SAM application provided with our example, since we don't have a front end to view, the link just takes you to a GET request for the `/services` API endpoint and should display some raw JSON in your browser.)\n\n![Link to live environment](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/link-live-environment.png){: .shadow.medium.center}\n\nThe `on_stop` property specifies what happens when you \"shut down\" the environment in GitLab CI. This can be done manually or by deleting the associated branch. In the case above, we have stopped behavior for dev environments linked to a separate job called `stop:dev`:\n\n```yaml\nstop:dev:\n stage: deploy-dev\n variables:\n GIT_STRATEGY: none\n \u003C\u003C: *dev_variables\n \u003C\u003C: *shutdown_script\n when: manual\n environment:\n name: review/$CI_COMMIT_REF_NAME\n action: stop\n only:\n - branches\n except:\n - master\n\n```\n\nThis job launches the `shutdown_script` script, which calls `aws cloudformation teardown` to clean up the SAM deployment.\n\nFor safety's sake, there is no automated teardown of staging or production environments.\n\n## Production releases\n\n![Production releases](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/production-releases.png){: .shadow.medium.center}\n\nWhen a change is merged into the master branch, the code is built, tested (including dependency scans) and deployed to the staging environment. This is a separate, stable environment that developers, QA, and others can use to verify changes before attempting to deploy in production.\n\n![Staging environment](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/staging-environment.png){: .shadow.medium.center}\n\nAfter deploying code to the staging environment, the pipeline will create a change set for the production stack, and then pause for a manual intervention. A human user must click a button in the Gitlab CI/CD \"Environments\" view to execute the final change set.\n\n## Now what?\n\nStep back and take a deep breath – that was a lot of information! Let's not lose sight of what we've done here: we've defined a secure, multi-account AWS deployment pipeline in our GitLab repo, integrated tests, builds and deployments, and successfully rolled a SAM-defined serverless app to the cloud. Not bad for a few lines of config!\n\nThe next step is to try this on your own. If you'd like to start with our sample \"AWS News\" application, you can simply run `sam init --location git+https://gitlab.com/gitlab-examples/aws-sam` to download the project on your local machine. The AWS News app contains a stripped-down, single-account version of the `gitlab-ci.yml` file discussed in this post, so you can try out deployments with minimal setup needed.\n\n## Further reading\n\nWe have barely scratched the surface of GitLab CI/CD and AWS SAM in this post. Here are some interesting readings if you would like to take your work to the next level:\n\n### SAM\n\n- [Implementing safe AWS Lambda deployments with AWS SAM and CodeDeploy](https://aws.amazon.com/blogs/compute/implementing-safe-aws-lambda-deployments-with-aws-codedeploy/)\n- [Running and debugging serverless applications locally using the AWS SAM CLI](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-test-and-debug.html)\n\n### GitLab CI\n\n- [Setting up a GitLab Runner on EC2](https://hackernoon.com/configuring-gitlab-ci-on-aws-ec2-using-docker-7c359d513a46)\n- [Scheduled pipelines](https://docs.gitlab.com/ee/ci/pipelines/schedules.html)\n- [ChatOps](https://docs.gitlab.com/ee/ci/chatops/)\n\nPlease [let me know](https://twitter.com/forrestbrazeal) if you have further questions!\n\n### About the guest author\n\nForrest Brazeal is an [AWS Serverless Hero](https://aws.amazon.com/developer/community/heroes/forrest-brazeal/). He currently works as a senior cloud architect at [Trek10](https://trek10.com), an AWS Advanced Consulting Partner. 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statement",{"items":682},[683,686,689],{"text":684,"config":685},"Terms",{"href":511,"dataGaName":512,"dataGaLocation":459},{"text":687,"config":688},"Cookies",{"dataGaName":521,"dataGaLocation":459,"id":522,"isOneTrustButton":31},{"text":690,"config":691},"Privacy",{"href":516,"dataGaName":517,"dataGaLocation":459},[693],{"id":694,"title":18,"body":8,"config":695,"content":697,"description":8,"extension":29,"meta":701,"navigation":31,"path":702,"seo":703,"stem":704,"__hash__":705},"blogAuthors/en-us/blog/authors/forrest-brazeal.yml",{"template":696},"BlogAuthor",{"name":18,"config":698},{"headshot":699,"ctfId":700},"","fbrazeal",{},"/en-us/blog/authors/forrest-brazeal",{},"en-us/blog/authors/forrest-brazeal","-LJoNl2kFQ2-t5P9UDj-5kdXlZaHlvc9b_rG5JBTI2w",[707,719,731],{"content":708,"config":717},{"title":709,"description":710,"authors":711,"heroImage":713,"date":714,"category":9,"tags":715,"body":716},"How 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.",[712],"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",[263,614,26],"The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":718,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":720,"config":729},{"title":721,"description":722,"authors":723,"heroImage":724,"date":725,"category":9,"tags":726,"body":728},"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.",[712],"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",[614,263,727],"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":730,"featured":31,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":732,"config":745},{"category":9,"tags":733,"body":736,"date":737,"updatedDate":738,"heroImage":739,"authors":740,"title":743,"description":744},[734,735,23],"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",[741,742],"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":31,"template":13,"slug":746},"migration-from-azure-devops-to-gitlab",{"promotions":748},[749,763,774],{"id":750,"categories":751,"header":753,"text":754,"button":755,"image":760},"ai-modernization",[752],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":756,"config":757},"Get your AI maturity score",{"href":758,"dataGaName":759,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":761},{"src":762},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":764,"categories":765,"header":766,"text":754,"button":767,"image":771},"devops-modernization",[727,560],"Are you just managing tools or shipping innovation?",{"text":768,"config":769},"Get your DevOps maturity score",{"href":770,"dataGaName":759,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":772},{"src":773},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":775,"categories":776,"header":778,"text":754,"button":779,"image":783},"security-modernization",[777],"security","Are you trading speed for security?",{"text":780,"config":781},"Get your security maturity score",{"href":782,"dataGaName":759,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":784},{"src":785},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":787,"blurb":788,"button":789,"secondaryButton":794},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":790,"config":791},"Get your free trial",{"href":792,"dataGaName":54,"dataGaLocation":793},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":497,"config":795},{"href":58,"dataGaName":59,"dataGaLocation":793},1772652096135]