[{"data":1,"prerenderedAt":793},["ShallowReactive",2],{"/en-us/blog/how-to-agentless-gitops-aws":3,"navigation-en-us":40,"banner-en-us":440,"footer-en-us":450,"blog-post-authors-en-us-Cesar Saavedra":688,"blog-related-posts-en-us-how-to-agentless-gitops-aws":702,"assessment-promotions-en-us":744,"next-steps-en-us":783},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":35,"tagSlugs":36,"__hash__":39},"blogPosts/en-us/blog/how-to-agentless-gitops-aws.yml","How To Agentless Gitops Aws",[7],"cesar-saavedra",null,"engineering",{"slug":11,"featured":12,"template":13},"how-to-agentless-gitops-aws",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How to use a push-based approach for GitOps with Terraform and AWS ECS and EC2","Learn how GitLab supports agentless approach for GitOps on AWS.",[18],"Cesar Saavedra","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663397/Blog/Hero%20Images/logoforblogpost.jpg","2021-08-10","In [part two of our GitOps series](/blog/how-to-agentless-gitops-vars/), we described how to use a push-based (or agentless) approach for [GitOps](/topics/gitops/) by using GitLab scripting capabilities as well as integrating infrastructure-as-code tools into GitOps pipelines. In this third blog post, we’ll also dig deep into how to use a push-based approach, but this time our focus will be on the integrations of Terraform, AWS ECS, and AWS EC2 in GitOps flows. This approach may be preferable when using infrastructure components that aren't Kubernetes, such as VMs, physical devices, and cloud-provider services.\n\nSimilar to Ansible – an agentless IT automation solution – Terraform can be leveraged by the scripting capabilities of GitLab to shape your infrastructure. GitLab also provides out-of-the-box integrations with Terraform, such as GitLab-managed Terraform state and Terraform plan reports in merge requests.\n\n## GitOps flows with GitLab and Terraform\n\nIn this section, we explain how to use GitLab and Terraform for a non-Kubernetes GitOps flow and Kubernetes GitOps.\n\n### GitLab and Terraform for non-K8s infrastructure\n\nGitLab leverages Terraform to provision a non-Kubernetes infrastructure component, namely a MySQL database running on AWS.\n\nNote: Ideally, the provisioning of a database should be an on-demand, self-service process that developers can just use. We use this scenario to illustrate a GitOps flow using a non-Kubernetes infrastructure component.\n\n#### How collaboration works in GitLab\n\nSasha, a developer, creates an issue and assigns the issue to Sidney, the database administrator, who then creates a Merge Request (MR) to start her work and invite collaboration with other stakeholders across the organization. Opening the MR automatically creates a feature branch for the GitLab project. Sidney uses Terraform to create an infrastructure-as-code configuration for the database, named `mysqlmain.tf`. The database happens to be an AWS RDS MySQL instance. The database Terraform configuration file should look like this:\n\n![Terraform configuration file for MySQL database](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/0-tf-mysqlmain-created.png){: .shadow.small.center.wrap-text}\nTerraform configuration file for MySQL database.\n\n\nTake note of the version of the database (`engine_version`), the database storage (`allocated_storage`), and the embedded database admin user (`username`) and password, in the image above.\n\nAs soon as Sidney adds the file `mysqlmain.tf` file to the feature branch, a pipeline is automatically executed by GitLab in the MR. As part of the review process, a \"Terraform plan\" is executed against the Terraform files and the output is attached to the MR as an artifact:\n\n![Terraform plan output attached to Merge Request](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/1-tf-report-in-MR.png){: .shadow.small.center.wrap-text}\nTerraform plan output attached to MR.\n\n\nIn the picture above, you can see the note \"1 Terraform report was generated in your pipelines\". You can click on the `View full log` button to see the output file of the \"Terraform plan\" command that was run against the new configuration file, as seen below:\n\n![Terraform plan output detailed log view](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/2-tf-plan-output.png){: .shadow.small.center.wrap-text}\nTerraform plan output detailed log view.\n\n\nThe Terraform output shows that a database will be created once this configuration file is applied to the infrastructure. The artifacts attached to an MR provide information that can help stakeholders review the proposed changes. The Terraform output in the MR fosters collaboration between stakeholders, and leads to infrastructure that is more consistent, resilient, reliable, and stable, and helps prevent unscheduled outages.\n\nIn the image below, we see how reviewers can collaborate in GitLab. The screenshow shows that the original requester, Sasha, notices that a database storage of 5 GB is too small, so she makes an inline suggestion to increase the database storage capacity to 10 GB.\n\n![Inline suggestion to increase database storage to 10GB](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/3-tf-inline-suggestion-by-Sasha.png){: .shadow.small.center.wrap-text}\nInline suggestion to increase database storage to 10GB.\n\n\nInline suggestions foster collaboration and help increase developer productivity suggested changes can be added with the click of a button.\n\nNext, Sidney invites DevOps engineer Devon to collaborate on the MR. Devon notices that the database version in the configuration file is not the latest one. He proceeds to make an inline suggestion proposing a more up-to-date version for Sidney to review:\n\n![Inline suggestion to update database version](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/4-tf-inline-suggestion-by-Devon.png){: .shadow.small.center.wrap-text}\nInline suggestion to update database version.\n\n\nSidney can monitor the discussion between code reviewers on the MR by tracking the number of unresolved threads. So far, there are four unresolved threads:\n\n![Number of unresolved threads displayed at the top of the MR](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/5-tf-unresolved-threads-for-Sidney.png){: .shadow.small.center.wrap-text}\nNumber of unresolved threads displayed at the top of the MR.\n\n\nSidney starts resolving the threads by following the convenient thread navigation provided by GitLab, which makes it easy for her to process each of the proposed review items. Sidney just needs to click \"Apply suggestion\" to accept an input from a reviewer:\n\n![Applying a suggestion with a single button click](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/6-tf-apply-inline-suggestion-by-Sidney.png){: .shadow.small.center.wrap-text}\nApplying a suggestion with one click.\n\n\nDevon suggested replacing the embedded database admin username and password with a parameter in the inline review, so Sidney replaces the embedded values with variables. The variable values will be managed by masked variables within GitLab:\n\n![Parameterizing variables in Terraform configuration file](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/7-tf-parameterizing-vars-by-Sidney.png){: .shadow.small.center.wrap-text}\nParameterizing variables in Terraform configuration file.\n\n\nOnce the threads are resolved and the stakeholders involved in thh MR finish collaborating, it's time to merge.\n\nLearn more about how GitLab fosters collaboration using the principles of GitOps in the video below:\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n \u003Ciframe src=\"https://www.youtube-nocookie.com/embed/onFpj_wvbLM\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n\nIn this next example, Sasha is the one merging the MR:\n\n![Merge Request with infrastructure updates being merged](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/8-tf-MR-merged.png){: .shadow.small.center.wrap-text}\nMR with infrastructure updates being merged.\n\n\nMerging automatically launches a pipeline that will apply the changes to the infrastructure:\n\n![GitOps pipeline completed execution](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/9-tf-pipeline-complete.png){: .shadow.small.center.wrap-text}\nGitOps pipeline completed execution.\n\n\n#### CI/CD with non-K8s infrastructure\n\nThe CI/CD pipeline in the previous example works by validating the infrastructure configuration files. Then the pipeline validates the proposed updates against the current state of the infrastructure. Finally, it applies the updates to the production infrastructure.\n\nRunning this GitOps flow results in a brand new MySQL database on AWS RDS:\n\n![A new MySQL database has been created via a GitOps flow](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/10-db-ready.png){: .shadow.small.center.wrap-text}\nA new MySQL database has been created via a GitOps flow.\n\n\nBy checking the details of the new MySQL database you can corroborate that the database storage is 10 GB and that the database version is the most current\"\n\n![Resulting MySQL database configuration from the collaboration of stakeholders](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/11-db-version-and-10g-storage.png){: .shadow.small.center.wrap-text}\nThe MySQL database configuration built by team member collaboration.\n\n\nIn the next section, we look at how a similar GitOps flow can be applied to a Kubernetes cluster.\n\n### GitLab and Terraform for K8s infrastructure\n\nWe skip past all the collaboration steps to focus on a change to the EKS cluster Terraform configuration file. In the picture below, a user is changing the minimum size of the autoscaling group of the EKS cluster from one to two:\n\n![Raising autoscaling group minimum to 2](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/12-worker-nodes-to-two.png){: .shadow.small.center.wrap-text}\nIncreasing autoscaling group minimum to two.\n\n\nWhen the stakeholder commits the change in the MR, a CI/CD pipeline validates the configuration, performs a plan against production, and applys the updates to the production infrastructure. After the pipeline finishes, the user can log into the Amazon EC2 console to verify that the EKS cluster now has a minimum of two nodes in its autoscaling group:\n\n![GitOps flow modified the number of worker nodes in K8s cluster](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/13-two-worker-nodes-on-AWS.png){: .shadow.small.center.wrap-text}\nGitOps flow modified the number of worker nodes in K8s cluster.\n\n\nSee this scenario in action by watching the [GitOps presentation](/topics/gitops/gitops-multicloud-deployments-gitlab/) on our GitOps topics page.\n\n## GitOps flows for non-K8s (like ECS, EC2)\n\nGitLab also provides Auto Deploy capabilities to streamline application deployment to ECS and EC2, so you can shape infrastructure as desired.\n\n### Deploying to Amazon ECS\n\nAfter creating your ECS cluster, GitLab can deliver your application and its infrastructure to the cluster by including the ECS Deployment template in your `gitlab-ci.yml`, using CI/CD.\n\n```yaml\ninclude:\nTemplate: AWS/Deploy-ECS.gitlab-ci.yml\n```\n\nNext, create the `ECS Task Definition` file in your project that specifies your app's infrastructure requirements, along with other details.\n\n![ECS Task Definition file snippet](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/14-ECS-taskdef-file.png){: .shadow.small.center.wrap-text}\nECS Task Definition file snippet.\n\n\nFinally, define the project variable that will drive the template:\n\n![Project variables required to auto-deploy to ECS](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/15-ECS-related-vars.png){: .shadow.small.center.wrap-text}\nProject variables required to auto-deploy to ECS.\n\n\nThe ECS deployment template does the rest, including support review pipelines.\n\n![Review pipeline in GitOps flow](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/16-ECS-review-pipeline.png){: .shadow.small.center.wrap-text}\nReview pipeline in GitOps flow.\n\n\nIn the review pipeline above, stakeholders can review the proposed changes before sending to production. The two screenshots below show different aspects of the proposed changes in the log output of the `review_fargate` job:\n\n![Configuring load balancers in ECS](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/17-review-fargate-log-begin.png){: .shadow.small.center.wrap-text}\nConfigure load balancers in ECS.\n\n\nSee the configuration for infrastructure components like load balancers in the image above. The image below shows infrastructure components like subnets, security groups, and the assignment of a public IP address:\n\n![Configuring subnets, security groups in ECS](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/18-review-fargate-log-middle.png){: .shadow.small.center.wrap-text}\nConfiguring subnets and security groups in ECS.\n\n\nOnce all stakeholders are done collaborating on a proposed change to the production infrastructure, the updates are applied using a CI/CD pipeline. Below is an example of this type of pipeline:\n\n![Applying infrastructure updates to production](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/19-ECS-prod-pipeline.png){: .shadow.small.center.wrap-text}\nApplying infrastructure updates to production.\n\n\nRead our documentation to learn more about [how GitLab users can Auto Deploy to ECS](https://docs.gitlab.com/ee/ci/cloud_deployment/#deploy-your-application-to-the-aws-elastic-container-service-ecs).\n\n### Deploying to Amazon EC2\n\nGitLab also provides a built-in template to provision infrastructure and deploy your applications to EC2 as part of Auto DevOps. The template:\n\n- Provisions infrastructure using AWS CloudFormation\n- Pushes application to S3\n- Deploys your application from S3 to EC2\n\nEach of these steps requires a JSON configuration file. Below is an example of a portion of a CloudFormation Stack JSON file used to create your infrastructure:\n\n![CloudFormation stack JSON snippet](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/20-EC2-portion-stack-file.png){: .shadow.small.center.wrap-text}\nCloudFormation stack JSON snippet.\n\n\nThe JSON used by the Auto Deploy template to push your application to S3 would look similar to this:\n\n![JSON to push application to S3](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/21-EC2-push-file.png){: .shadow.small.center.wrap-text}\nJSON to push application to S3.\n\n\nAnd the file used for the actual deployment of your application from S3 to EC2 would be like the following:\n\n![JSON to deploy application to EC2](https://about.gitlab.com/images/blogimages/how-to-agentless-gitops-aws/22-EC2-deploy-file.png){: .shadow.small.center.wrap-text}\nJSON to deploy application to EC2.\n\n\nAfter creating these files, you need to create the following variables in your project - displayed here with some sample values:\n\n```yaml\nvariables:\n CI_AWS_CF_CREATE_STACK_FILE: 'aws/cf_create_stack.json'\n CI_AWS_S3_PUSH_FILE: 'aws/s3_push.json'\n CI_AWS_EC2_DEPLOYMENT_FILE: 'aws/create_deployment.json'\n CI_AWS_CF_STACK_NAME: 'YourStackName'\n\n```\n\nThe last step is to include the template in your `.gitlab-ci.yml` file:\n\n```yaml\ninclude:\n - template: AWS/CF-Provision-and-Deploy-EC2.gitlab-ci.yml\n\n```\n\nMore details on [how GitLab uses Auto Deploy to EC2 are available in the documentation](https://docs.gitlab.com/ee/ci/cloud_deployment/#provision-and-deploy-to-your-aws-elastic-compute-cloud-ec2).\n\n## Agent or agentless: GitLab has your GitOps flows covered\n\nWhether your situation calls for an agent-based/pull-approach to doing GitOps, or for an agentless/push-approach, GitLab has your back. GitLab offers the flexibility to choose the approach to GitOps that best fits your specific projects or applications. GitLab also supports many types of infrastructures – from physical components and virtual machines, Kubernetes and containers, as well as infrastructure-as-code tools like Terraform, Ansible, and AWS Cloud Formation.\n",[23,24,25],"GitOps","DevOps","demo","yml",{},true,"/en-us/blog/how-to-agentless-gitops-aws",{"title":31,"description":16,"ogTitle":31,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":32,"ogSiteName":33,"ogType":34,"canonicalUrls":32},"How to Use Push-Based GitOps with Terraform & AWS ECS/EC2","https://about.gitlab.com/blog/how-to-agentless-gitops-aws","https://about.gitlab.com","article","en-us/blog/how-to-agentless-gitops-aws",[37,38,25],"gitops","devops","T23qJpztJ-GLBNacXSrzYMFvlDc8dd8dO6rZhVKZ-vk",{"data":41},{"logo":42,"freeTrial":47,"sales":52,"login":57,"items":62,"search":370,"minimal":401,"duo":420,"pricingDeployment":430},{"config":43},{"href":44,"dataGaName":45,"dataGaLocation":46},"/","gitlab logo","header",{"text":48,"config":49},"Get free 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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.",[708],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[262,610,712],"open source","The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":715,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":717,"config":726},{"title":718,"description":719,"authors":720,"heroImage":721,"date":722,"category":9,"tags":723,"body":725},"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.",[708],"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",[610,262,724],"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":727,"featured":28,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":729,"config":742},{"category":9,"tags":730,"body":733,"date":734,"updatedDate":735,"heroImage":736,"authors":737,"title":740,"description":741},[731,732,109],"tutorial","git","\nEnterprise teams are increasingly migrating from Azure DevOps to GitLab to gain strategic advantages and accelerate secure software delivery. \n\n\n- GitLab comes with integrated controls, policies, and [compliance frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) that allow organizations to implement software delivery standards at scale. This is especially important for regulated industries.\n\n- [Security testing](https://docs.gitlab.com/user/application_security/) is embedded in the pipeline and results show in the developer workflow, including static application security testing (SAST), source code analysis (SCA), dynamic application security testing (DAST), infrastructure-as-code scanning (IaC), container scanning, and API scanning.\n\n- [AI capabilities](https://about.gitlab.com/gitlab-duo-agent-platform/) across the full software delivery lifecycle include advanced agent orchestration and customizable flows to support how your organizational teams work.\n\n\nGitLab's open-source, open-core approach, flexible deployment options such as single-tenant dedicated and self-managed, and truly unified platform eliminate integration complexity and security gaps. \n\n\nFor teams facing mounting pressure to accelerate delivery while strengthening security posture and maintaining regulatory compliance, GitLab represents not just a migration but a platform evolution.\n\n\nMigrating from Azure DevOps to GitLab can seem like a daunting task, but with the right approach and tools, it can be a smooth and efficient process. This guide will walk you through the steps needed to successfully migrate your projects, repositories, and pipelines from Azure DevOps to GitLab.\n\n\n## Overview\n\nGitLab provides both [Congregate](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/) (maintained by [GitLab Professional Services](https://about.gitlab.com/professional-services/) organization) and [a built-in Git repository import](https://docs.gitlab.com/user/project/import/repo_by_url/) for migrating projects from Azure DevOps (ADO). These options support repository-by-repository or bulk migration and preserve git commit history, branches, and tags. With Congregate and professional services tools, we support additional assets such as wikis, work items, CI/CD variables, container images, packages, pipelines, and more (see this [feature matrix](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/blob/master/customer/ado-migration-features-matrix.md)). Use this guide to plan and execute your migration and complete post-migration follow-up tasks.\n\n\nEnterprises migrating from ADO to GitLab commonly follow a multi-phase approach:\n\n\n- Migrate repositories from ADO to GitLab using Congregate or GitLab's built-in repository migration.\n\n- Migrate pipelines from Azure Pipelines to GitLab CI/CD.\n\n- Migrate remaining assets such as boards, work items, and artifacts to GitLab Issues, Epics, and the Package and Container Registries.\n\n\nHigh-level migration phases:\n\n\n```mermaid\ngraph LR\n subgraph Prerequisites\n direction TB\n A[\"Set up identity provider (IdP) and\u003Cbr/>provision users\"]\n A --> B[\"Set up runners and\u003Cbr/>third-party integrations\"]\n B --> I[\"Users enablement and\u003Cbr/>change management\"]\n end\n \n subgraph MigrationPhase[\"Migration phase\"]\n direction TB\n C[\"Migrate source code\"]\n C --> D[\"Preserve contributions and\u003Cbr/> format history\"]\n D --> E[\"Migrate work items and\u003Cbr/>map to \u003Ca href=\"https://docs.gitlab.com/topics/plan_and_track/\">GitLab Plan \u003Cbr/>and track work\"]\n end\n \n subgraph PostMigration[\"Post-migration steps\"]\n direction TB\n F[\"Create or translate \u003Cbr/>ADO pipelines to GitLab CI\"]\n F --> G[\"Migrate other assets\u003Cbr/>packages and container images\"]\n G --> H[\"Introduce \u003Ca href=\"https://docs.gitlab.com/user/application_security/secure_your_application/\">security\u003C/a> and\u003Cbr/>SDLC improvements\"]\n end\n \n Prerequisites --> MigrationPhase\n MigrationPhase --> PostMigration\n\n style A fill:#FC6D26\n style B fill:#FC6D26\n style I fill:#FC6D26\n style C fill:#8C929D\n style D fill:#8C929D\n style E fill:#8C929D\n style F fill:#FFA500\n style G fill:#FFA500\n style H fill:#FFA500\n```\n\n\n## Planning your migration\n\n\n**To plan your migration, ask these questions:**\n\n\n- How soon do we need to complete the migration?\n\n- Do we understand what will be migrated?\n\n- Who will run the migration?\n\n- What organizational structure do we want in GitLab?\n\n- Are there any constraints, limitations, or pitfalls that need to be taken into account?\n\n\nDetermine your timeline, as it will largely dictate your migration approach. Identify champions or groups familiar with both ADO and GitLab platforms (such as early adopters) to help drive adoption and provide guidance.\n\n\n**Inventory what you need to migrate:**\n\n\n- The number of repositories, pull requests, and contributors\n\n- The number and complexity of work items and pipelines\n\n- Repository sizes and dependency relationships\n\n- Critical integrations and runner requirements (agent pools with specific capabilities)\n\n\nUse GitLab Professional Services's [Evaluate](https://gitlab.com/gitlab-org/professional-services-automation/tools/utilities/evaluate#beta-azure-devops) tool to produce a complete inventory of your entire Azure DevOps organization, including repositories, PR counts, contributor lists, number of pipelines, work items, CI/CD variables and more. If you're working with the GitLab Professional Services team, share this report with your engagement manager or technical architect to help plan the migration.\n\n\nMigration timing is primarily driven by pull request count, repository size, and amount of contributions (e.g. comments in PR, work items, etc). For example, 1,000 small repositories with few PRs and limited contributors can migrate much faster than a smaller set of repositories containing tens of thousands of PRs and thousands of contributors. Use your inventory data to estimate effort and plan test runs before proceeding with production migrations.\n\n\nCompare inventory against your desired timeline and decide whether to migrate all repositories at once or in batches. If teams cannot migrate simultaneously, batch and stagger migrations to align with team schedules. For example, in Professional Services engagements, we organize migrations into waves of 200-300 projects to manage complexity and respect API rate limits, both in [GitLab](https://docs.gitlab.com/security/rate_limits/) and [ADO](https://learn.microsoft.com/en-us/azure/devops/integrate/concepts/rate-limits?view=azure-devops).\n\n\nGitLab's built-in [repository importer](https://docs.gitlab.com/user/project/import/repo_by_url/) migrates Git repositories (commits, branches, and tags) one-by-one. Congregate is designed to preserve pull requests (known in GitLab as merge requests), comments, and related metadata where possible; the simple built-in repository import focuses only on the Git data (history, branches, and tags).\n\n\n**Items that typically require separate migration or manual recreation:**\n\n\n- Azure Pipelines - create equivalent GitLab CI/CD pipelines (consult with [CI/CD YAML](https://docs.gitlab.com/ci/yaml/) and/or with [CI/CD components](https://docs.gitlab.com/ci/components/)). Alternatively, consider using AI-based pipeline conversion available in Congregate.\n\n- Work items and boards - map to GitLab Issues, Epics, and Issue Boards.\n\n- Artifacts, container images (ACR) - migrate to GitLab Package Registry or Container Registry.\n\n- Service hooks and external integrations - recreate in GitLab.\n\n- [Permissions models](https://docs.gitlab.com/user/permissions/) differ between ADO and GitLab; review and plan permissions mapping rather than assuming exact preservation.\n\n\nReview what each tool (Congregate vs. built-in import) will migrate and choose the one that fits your needs. Make a list of any data or integrations that must be migrated or recreated manually.\n\n\n**Who will run the migration?**\n\n\nMigrations are typically run by a GitLab group owner or instance administrator, or by a designated migrator who has been granted the necessary permissions on the destination group/project. Congregate and the GitLab import APIs require valid authentication tokens for both Azure DevOps and GitLab.\n\n\n- Decide whether a group owner/admin will perform the migrations or whether you will grant a specific team/person delegated access.\n\n- Ensure the migrator has correctly configured personal access tokens (Azure DevOps and GitLab) with the scopes required by your chosen migration tool (for example, api/read_repository scopes and any tool-specific requirements). \n\n- Test tokens and permissions with a small pilot migration.\n\n**Note:** Congregate leverages file-based import functionality for ADO migrations and requires instance administrator permissions to run ([see our documentation](https://docs.gitlab.com/user/project/settings/import_export/#migrate-projects-by-uploading-an-export-file)). If you are migrating to GitLab.com, consider engaging Professional Services. For more information, see the [Professional Services Full Catalog](https://about.gitlab.com/professional-services/catalog/). Non-admin account cannot preserve contribution attribution!\n\n\n**What organizational structure do we want in GitLab?**\n\nWhile it's possible to map ADO structure directly to GitLab structure, it's recommended to rationalize and simplify the structure during migration. Consider how teams will work in GitLab and design the structure to facilitate collaboration and access management. Here is a way to think about mapping ADO structure to GitLab structure:\n\n\n```mermaid\ngraph TD\n subgraph GitLab\n direction TB\n A[\"Top-level Group\"]\n B[\"Subgroup (optional)\"]\n C[\"Projects\"]\n A --> B\n A --> C\n B --> C\n end\n\n subgraph AzureDevOps[\"Azure DevOps\"]\n direction TB\n F[\"Organizations\"]\n G[\"Projects\"]\n H[\"Repositories\"]\n F --> G\n G --> H\n end\n\n style A fill:#FC6D26\n style B fill:#FC6D26\n style C fill:#FC6D26\n style F fill:#8C929D\n style G fill:#8C929D\n style H fill:#8C929D\n```\n\nRecommended approach:\n\n\n- Map each ADO organization to a GitLab group (or a small set of groups), not to many small groups. Avoid creating a GitLab group for every ADO team project. Use migration as an opportunity to rationalize your GitLab structure.\n\n- Use subgroups and project-level permissions to group related repositories.\n\n- Manage access to sets of projects by using GitLab groups and group membership (groups and subgroups) rather than one group per team project.\n\n- Review GitLab [permissions](https://docs.gitlab.com/ee/user/permissions.html) and consider [SAML Group Links](https://docs.gitlab.com/user/group/saml_sso/group_sync/) to implement an enterprise RBAC model for your GitLab instance (or a GitLab.com namespace).\n\n\n**ADO Boards and work items: State of migration**\n\n\nIt's important to understand how work items migrate from ADO into GitLab Plan (issues, epics, and boards).\n\n\n- ADO Boards and work items map to GitLab Issues, Epics, and Issue Boards. Plan how your workflows and board configurations will translate.\n\n- ADO Epics and Features become GitLab Epics.\n\n- Other work item types (e.g., user stories, tasks, bugs) become project-scoped issues.\n\n- Most standard fields are preserved; selected custom fields can be migrated when supported.\n\n- Parent-child relationships are retained so Epics reference all related issues.\n\n- Links to pull requests are converted to merge request links to maintain development traceability.\n\n\nExample: Migration of an individual work item to a GitLab Issue, including field accuracy and relationships:\n\n\n![Example: Migration of an individual work item to a GitLab Issue](https://res.cloudinary.com/about-gitlab-com/image/upload/v1764769188/ztesjnxxfbwmfmtckyga.png)\n\n\nBatching guidance:\n\n\n- If you need to run migrations in batches, use your new group/subgroup structure to define batches (for example, by ADO organization or by product area).\n\n- Use inventory reports to drive batch selection and test each batch with a pilot migration before scaling.\n\n\n**Pipelines migration**\n\n\nCongregate [recently introduced](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/merge_requests/1298) AI-powered conversion for multi-stage YAML pipelines from Azure DevOps to GitLab CI/CD. This automated conversion works best for simple, single-file pipelines and is designed to provide a working starting point rather than a production-ready `.gitlab-ci.yml` file. The tool generates a functionally equivalent GitLab pipeline that you can then refine and optimize for your specific needs.\n\n\n- Converts Azure Pipelines YAML to `.gitlab-ci.yml` format automatically.\n\n- Best suited for straightforward, single-file pipeline configurations.\n\n- Provides a boilerplate to accelerate migration, not a final production artifact.\n\n- Requires review and adjustment for complex scenarios, custom tasks, or enterprise requirements.\n\n- Does not support Azure DevOps classic release pipelines — [convert these to multi-stage YAML](https://learn.microsoft.com/en-us/azure/devops/pipelines/release/from-classic-pipelines?view=azure-devops) first.\n\n\nRepository owners should review the [GitLab CI/CD documentation](https://docs.gitlab.com/ci/) to further optimize and enhance their pipelines after the initial conversion.\n\n\nExample of converted pipelines:\n\n\n```yml \n\n# azure-pipelines.yml\n\ntrigger:\n - main\n\nvariables:\n imageName: myapp\n\nstages:\n - stage: Build\n jobs:\n - job: Build\n pool:\n vmImage: 'ubuntu-latest'\n steps:\n - checkout: self\n\n - task: Docker@2\n displayName: Build Docker image\n inputs:\n command: build\n repository: $(imageName)\n Dockerfile: '**/Dockerfile'\n tags: |\n $(Build.BuildId)\n\n - stage: Test\n jobs:\n - job: Test\n pool:\n vmImage: 'ubuntu-latest'\n steps:\n - checkout: self\n\n # Example: run tests inside the container\n - script: |\n docker run --rm $(imageName):$(Build.BuildId) npm test\n displayName: Run tests\n\n - stage: Push\n jobs:\n - job: Push\n pool:\n vmImage: 'ubuntu-latest'\n steps:\n - checkout: self\n\n - task: Docker@2\n displayName: Login to ACR\n inputs:\n command: login\n containerRegistry: '\u003Cyour-acr-service-connection>'\n\n - task: Docker@2\n displayName: Push image to ACR\n inputs:\n command: push\n repository: $(imageName)\n tags: |\n $(Build.BuildId)\n\n```\n\n```yaml\n\n# .gitlab-ci.yml\n\nvariables:\n imageName: myapp\n\nstages:\n - build\n - test\n - push\n\nbuild:\n stage: build\n image: docker:latest\n services:\n - docker:dind\n script:\n - docker build -t $imageName:$CI_PIPELINE_ID -f $(find . -name Dockerfile) .\n only:\n - main\n\ntest:\n stage: test\n image: docker:latest\n services:\n - docker:dind\n script:\n - docker run --rm $imageName:$CI_PIPELINE_ID npm test\n only:\n - main\n\npush:\n stage: push\n image: docker:latest\n services:\n - docker:dind\n before_script:\n - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n script:\n - docker tag $imageName:$CI_PIPELINE_ID $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n - docker push $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n only:\n - main\n\n```\n\n**Final checklist:**\n\n\n- Decide timeline and batch strategy.\n\n- Produce a full inventory of repositories, PRs, and contributors.\n\n- Choose Congregate or the built-in import based on scope (PRs and metadata vs. Git data only).\n\n- Decide who will run migrations and ensure tokens/permissions are configured.\n\n- Identify assets that must be migrated separately (pipelines, work items, artifacts, and hooks) and plan those efforts.\n\n- Run pilot migrations, validate results, then scale according to your plan.\n\n\n## Running your migrations\n\n\nAfter planning, execute migrations in stages, starting with trial runs. Trial migrations help surface org-specific issues early and let you measure duration, validate outcomes, and fine-tune your approach before production.\n\n\nWhat trial migrations validate:\n\n\n- Whether a given repository and related assets migrate successfully (history, branches, tags; plus MRs/comments if using Congregate)\n\n- Whether the destination is usable immediately (permissions, runners, CI/CD variables, integrations)\n\n- How long each batch takes, to set schedules and stakeholder expectations\n\n\nDowntime guidance:\n\n\n- GitLab's built-in Git import and Congregate do not inherently require downtime.\n\n- For production waves, freeze changes in ADO (branch protections or read-only) to avoid missed commits, PR updates, or work items created mid-migration.\n\n- Trial runs do not require freezes and can be run anytime.\n\n\nBatching guidance:\n\n\n- Run trial batches back-to-back to shorten elapsed time; let teams validate results asynchronously.\n\n- Use your planned group/subgroup structure to define batches and respect API rate limits.\n\n\nRecommended steps:\n\n\n1. Create a test destination in GitLab for trials:\n\n\n - GitLab.com: create a dedicated group/namespace (for example, my-org-sandbox)\n\n - Self-managed: create a top-level group or a separate test instance if needed\n\n\n2. Prepare authentication:\n\n\n - Azure DevOps PAT with required scopes.\n\n - GitLab Personal Access Token with api and read_repository (plus admin access for file-based imports used by Congregate).\n\n\n3. Run trial migrations:\n\n\n - Repos only: use GitLab's built-in import (Repo by URL)\n\n - Repos + PRs/MRs and additional assets: use Congregate\n\n\n4. Post-trial follow-up:\n\n\n - Verify repo history, branches, tags; merge requests (if migrated), issues/epics (if migrated), labels, and relationships.\n\n - Check permissions/roles, protected branches, required approvals, runners/tags, variables/secrets, integrations/webhooks.\n\n - Validate pipelines (`.gitlab-ci.yml`) or converted pipelines where applicable.\n\n\n5. Ask users to validate functionality and data fidelity.\n\n6. Resolve issues uncovered during trials and update your runbooks.\n\n7. Network and security:\n\n\n - If your destination uses IP allow lists, add the IPs of your migration host and any required runners/integrations so imports can succeed.\n\n\n8. Run production migrations in waves:\n\n\n - Enforce change freezes in ADO during each wave.\n\n - Monitor progress and logs; retry or adjust batch sizes if you hit rate limits.\n\n\n9. Optional: remove the sandbox group or archive it after you finish.\n\n\n\u003Cfigure class=\"video_container\">\n \u003Ciframe src=\"https://www.youtube.com/embed/ibIXGfrVbi4?si=ZxOVnXjCF-h4Ne0N\" frameborder=\"0\" allowfullscreen=\"true\">\u003C/iframe>\n\u003C/figure>\n\n\n## Terminology reference for GitLab and Azure DevOps\n\n| GitLab | Azure DevOps | Similarities & Key Differences |\n| ---------------------------------------------------------------- | -------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Group | Organization | Top-level namespace, membership, policies. ADO org contains Projects; GitLab Group contains Subgroups and Projects. |\n| Group or Subgroup | Project | Logical container, permissions boundary. ADO Project holds many repos; GitLab Groups/Subgroups organize many Projects. |\n| Project (includes a Git repo) | Repository (inside a Project) | Git history, branches, tags. In GitLab, a \"Project\" is the repo plus issues, CI/CD, wiki, etc. One repo per Project. |\n| Merge Request (MR) | Pull Request (PR) | Code review, discussions, approvals. MR rules include approvals, required pipelines, code owners. |\n| Protected Branches, MR Approval Rules, Status Checks | Branch Policies | Enforce reviews and checks. GitLab combines protections + approval rules + required status checks. |\n| GitLab CI/CD | Azure Pipelines | YAML pipelines, stages/jobs, logs. ADO also has classic UI pipelines; GitLab centers on .gitlab-ci.yml. |\n| .gitlab-ci.yml | azure-pipelines.yml | Defines stages/jobs/triggers. Syntax/features differ; map jobs, variables, artifacts, and triggers. |\n| Runners (shared/specific) | Agents / Agent Pools | Execute jobs on machines/containers. Target via demands (ADO) vs tags (GitLab). Registration/scoping differs. |\n| CI/CD Variables (project/group/instance), Protected/Masked | Pipeline Variables, Variable Groups, Library | Pass config/secrets to jobs. GitLab supports group inheritance and masking/protection flags. |\n| Integrations, CI/CD Variables, Deploy Keys | Service Connections | External auth to services/clouds. Map to integrations or variables; cloud-specific helpers available. |\n| Environments & Deployments (protected envs) | Environments (with approvals) | Track deploy targets/history. Approvals via protected envs and manual jobs in GitLab. |\n| Releases (tag + notes) | Releases (classic or pipelines) | Versioned notes/artifacts. GitLab Release ties to tags; deployments tracked separately. |\n| Job Artifacts | Pipeline Artifacts | Persist job outputs. Retention/expiry configured per job or project. |\n| Package Registry (NuGet/npm/Maven/PyPI/Composer, etc.) | Azure Artifacts (NuGet/npm/Maven, etc.) | Package hosting. Auth/namespace differ; migrate per package type. |\n| GitLab Container Registry | Azure Container Registry (ACR) or others | OCI images. GitLab provides per-project/group registries. |\n| Issue Boards | Boards | Visualize work by columns. GitLab boards are label-driven; multiple boards per project/group. |\n| Issues (types/labels), Epics | Work Items (User Story/Bug/Task) | Track units of work. Map ADO types/fields to labels/custom fields; epics at group level. |\n| Epics, Parent/Child Issues | Epics/Features | Hierarchy of work. Schema differs; use epics + issue relationships. |\n| Milestones and Iterations | Iteration Paths | Time-boxing. GitLab Iterations (group feature) or Milestones per project/group. |\n| Labels (scoped labels) | Area Paths | Categorization/ownership. Replace hierarchical areas with scoped labels. |\n| Project/Group Wiki | Project Wiki | Markdown wiki. Backed by repos in both; layout/auth differ slightly. |\n| Test reports via CI, Requirements/Test Management, integrations | Test Plans/Cases/Runs | QA evidence/traceability. No 1:1 with ADO Test Plans; often use CI reports + issues/requirements. |\n| Roles (Owner/Maintainer/Developer/Reporter/Guest) + custom roles | Access levels + granular permissions | Control read/write/admin. Models differ; leverage group inheritance and protected resources. |\n| Webhooks | Service Hooks | Event-driven integrations. Event names/payloads differ; reconfigure endpoints. |\n| Advanced Search | Code Search | Full-text repo search. Self-managed GitLab may need Elasticsearch/OpenSearch for advanced features. |\n","2025-12-03","2026-01-16","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749658924/Blog/Hero%20Images/securitylifecycle-light.png",[738,739],"Evgeny Rudinsky","Michael Leopard","Guide: Migrate from Azure DevOps to GitLab","Learn how to carry out the full migration from Azure DevOps to GitLab using GitLab Professional Services migration tools — from planning and execution to post-migration follow-up tasks.",{"featured":28,"template":13,"slug":743},"migration-from-azure-devops-to-gitlab",{"promotions":745},[746,760,771],{"id":747,"categories":748,"header":750,"text":751,"button":752,"image":757},"ai-modernization",[749],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":753,"config":754},"Get your AI maturity score",{"href":755,"dataGaName":756,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":758},{"src":759},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":761,"categories":762,"header":763,"text":751,"button":764,"image":768},"devops-modernization",[724,556],"Are you just managing tools or shipping innovation?",{"text":765,"config":766},"Get your DevOps maturity score",{"href":767,"dataGaName":756,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":772,"categories":773,"header":775,"text":751,"button":776,"image":780},"security-modernization",[774],"security","Are you trading speed for security?",{"text":777,"config":778},"Get your security maturity score",{"href":779,"dataGaName":756,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":784,"blurb":785,"button":786,"secondaryButton":791},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":787,"config":788},"Get your free trial",{"href":789,"dataGaName":51,"dataGaLocation":790},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":496,"config":792},{"href":55,"dataGaName":56,"dataGaLocation":790},1772652068982]