[{"data":1,"prerenderedAt":822},["ShallowReactive",2],{"/en-us/blog/whiteboarding-remote-work-superpower":3,"navigation-en-us":39,"banner-en-us":449,"footer-en-us":459,"blog-post-authors-en-us-Darwin Sanoy":701,"blog-related-posts-en-us-whiteboarding-remote-work-superpower":717,"blog-promotions-en-us":759,"next-steps-en-us":812},{"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":34,"tagSlugs":35,"__hash__":38},"blogPosts/en-us/blog/whiteboarding-remote-work-superpower.yml","Whiteboarding Remote Work Superpower",[7],"darwin-sanoy",null,"engineering",{"slug":11,"featured":12,"template":13},"whiteboarding-remote-work-superpower",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Virtual whiteboarding is a remote work super power","Want to master a collective understanding of technical explanations remotely? Learn how to use virtual whiteboards to their maximum capabilities in this tutorial.",[18],"Darwin Sanoy","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682431/Blog/Hero%20Images/kvalifik-5Q07sS54D0Q-unsplash.jpg","2022-09-01","\n\nAt one point in my career I had a solo business in technology training. During this time, I went through a transition from live, in-person classes to live-delivered virtual classes. One of the things that I dearly missed in virtual delivery was unpacking explanations through whiteboarding. The difference in the speed and completeness of achieving common understanding across the group was very evident by the nature of the questions and discussions that occured immediately afterward.\n\nAt that time, it was difficult to find solutions that enabled me to do this as fluidly as an in-person classroom experience. I persisted and came up with an elaborate solution involving software and hardware – but the results of a fluid whiteboarding experience for both presenter and participants were preserved.\n\n## Explaining and collaborating through drawings\n\n“[The Back of the Napkin: Solving Problems and Selling Ideas with Pictures](https://www.penguinrandomhouse.com/books/300247/the-back-of-the-napkin-expanded-edition-by-dan-roam/)” is a great book on the topic of leveraging drawing in business meetings. The title contains two main themes. “Selling Ideas” is about explaining something you already understand in a highly effective way that creates shared understanding. “Solving Problems” is a very different mode, that of collaborating to create a new visual model that documents the structure of a problem or envisions a new solution. While there are variations on these two themes, these appear to be two most fundamental modes of using drawing in a group context.\n\n## The importance of progressive disclosure in understanding technical explanations\n\nTechnical explanation is challenging on its own - but the situation is made much worse by presenting complex visuals fully formed. Using a whiteboard for the same explanation leverages the progressive disclosure element of storytelling and overlays it on a technical visualization. This has a fundamental effect of enhancing understanding because when a complex visual appears completely formed, the visual cortex hijacks all neurological attention resources (including listening) as it attempts to make sense of the scene. Progressive disclosure allows the minds of listeners to focus on the verbal explanation because only the component being described is visible. As the narrator reveals the next chunk by drawing while explaining the relationship to the last chunk, the audience naturally shifts their full attention.\n\nYou could think of this effect as being the same as what a cartoon strip does to create a sense of progressive disclosure. By simply framing the scenes, our mind automatically focuses on one frame at a time in the intended sequence. The difference with technical explanations is that the final view is extremely informative without frames - it paints a global picture of the sum of the parts.\n\nThe magic of infographics is also the enforcement of progressive disclosure on their topic matter by purposely creating a visual so long that it must be scrolled. Frequently they are also organized around a natural or contrived timeline that the disclosure of the component parts progresses through. They frequently also create frames through visual effects such as lines, shapes, and whitespace.\n\nTechnical visualizations frequently have the characteristic that a big-picture visual is very valuable for complete understanding. Progressive disclosure resolves the contradictory requirements for both a “parts-level understanding” and a “big-picture understanding” of a technical design visualization. This is accomplished by layering up the big picture through many bite-sized explanations - exactly like how the mind's eye creates the world of a story when it is narrated in a sequence of small parts.\n\nWhiteboarding, by nature, can only be done as progressive disclosure and, in doing so, it transforms technical explanation into much more digestible and memorable frames for the audience to consume.\n\n## Maintaining a common understanding of the composite vision\n\nIn order to have the best chance to foster a \"group creative flow,\" everyone who is collaborating needs to maintain a common understanding of the composite vision as rapidly emerging ideas and insights are iteratively worked in. Whiteboarding fulfills this need in a way that is not distracting to the effort because a visualization of the vision is maintained in real-time during collaboration.\n\nFrequently group insights compound on each other as ideas are expanded by building on idea expressed by someone else in the group. Whiteboarding provides a real-time composite visualization which accelerates more new and valuable insights. Drawing frequently enables collaborators to draw things they can't find the words for at the moment. If the conditions are right, there is the potential for a snowball effect of synergistic ideas being incorporated into the composite whole.\n\nThis is where the need for the mechanism for holding the common understanding needs to be fluid and non-distracting.\n\n## Solutions architecture requires both technical explanation and collaboration\n\nIn my job as a Solutions Architect, it turns out that explanation of technical visuals and collaboration in creating technical designs are critical to making helpful contributions to colleagues, customers, and partners.\n\nIt is truly amazing how much more quickly a group of people can get on the same page and innovate when whiteboarding is available.\n\nWhen there are language barriers, it takes on an even higher value as visuals are not dependent on language and can help store the real-time common understanding between different language speakers. Multiple times, I’ve found that speakers who are working through a translator get excited and are emboldened to start talking in English (my native tongue). Generally, the technical terms are recognized in any language and adding them to the diagram fuels more mutual understanding.\n\nWhen I came to GitLab as a Solutions Architect, I, once again, began to experiment with ways to make fluid whiteboarding easy to do in any meeting.\n\n## Better than real whiteboards for in-person meetings\n\nOccasionally, when working hard for one objective, you accidentally achieve some objectives you didn’t even know you should or could have. This is known as serendipity.\n\nThis is what has happened in my pursuit of very fluid virtual whiteboarding. I found virtual whiteboarding handles a lot of logistics and practical considerations for whiteboarding for in-person meetings such as:\n\n* Verifying availability of whiteboards at a meeting venue\n* Simultaneous whiteboards and computer projection visibility (I’ve been in rooms where you had to stop projecting use the whiteboard)\n* You don’t even need a projector if you do an in-person virtual meeting to share your screen\n* Marker management - smell, mess, dried-out markers\n* The inability to preserve every whiteboard that you draw due to needing to erase for the next one\n* The inability to electronically store or share the visuals\n\n## The challenges of hardware and software selection\n\nI wish I could honestly say the process of putting together a fluid virtual whiteboard setup is now easy but I have not found that to be the case.\n\n### Mental flow requires fluid technology\n\nWhether explaining or collaborating the concept of mental flow is very critical. If the need for flow is interrupted by things that should be transparent, it is frustrating for everyone and the audience quickly loses attention. It interrupts the thoughts of both the whiteboarder and the participants.\n\nThink of the times that someone starts whiteboarding and the one and only marker goes dry and they have to hunt for one. Virtual whiteboarding can actually make the problem of interrupting flow much worse. This is because if there are delays in the hardware or software, the shape of what you are drawing gets incorrectly “smoothed”.\n\nA lack of fluidity will generally make your shapes challenging to draw and then you slow down to allow the system to recognize your strokes and, well, it’s not fluid anymore - it’s distracting and effortful. And the rending of shapes aren’t the worst of it, when trying to add text to label diagram parts, lack of fluidity causes the smaller strokes of text to be unrecognizable. A lack of fluid drawing completely kills the presenters desire to use drawing and the audiences desire to listen to the pictures.\n\n### Fluidity of the drawing activity\n\nIn trying to devise a cost-effective, yet fluid, setup I’ve tried all the shortcuts - such as using capacitive touchscreens with a stylus and using web apps as the primary whiteboarding software. Both of these are deal breakers for me because after trying many instances on these two options, they just never work out to have sufficient fluidity.\n\nSo here are the constraints I ended up adopting to make drawing itself very fluid:\n\n* **Use an iOS or Android mobile platform tablet** - as it has the following advantages:\n    * Native mobile apps are much, much more fluid than web apps.\n    * Many more native software options than there are for laptops.\n    * More modular and cheaper hardware in the long run than attempting to gain these capabilities in a laptop or desktop.\n* **Must support active pen technology** - capacitive touchscreens, even with a stylus, don’t cut it. When working rapidly, the smoothing algorithms aren’t very smooth - this makes drawing shapes difficult, but more importantly it makes writing words especially difficult.\n* **Having a stylus that is the correct length and thickness** is important for fluid writing and drawing. The stylus that comes with tablets is frequently not the conventional length or thickness of real pens or pencils.\n\n### Fluidity of integrating the act of drawing into virtual meetings\n\nUsing drawing needs to be easy for the meeting host and for the participants.\n\n* For easy virtual sharing, it helps if the native app also has a collaborative web app that updates quickly as it avoids the complications of joining the tablet to the meeting and sharing from there.\n* This enables other meeting participants to whiteboard on the same virtual whiteboard without specialized hardware.\n* Some systems allow guests to join and whiteboard without having to setup an account.\n\n### Fluidity of availability of whiteboarding across teams\n\nThere are multiple elements of what will ensure fluid whiteboarding is available to everyone for collaborations. A primary one is cost, followed by local device availability of active pen tablet options in international markets. Thankfully, the applications covered later support both iOS and Android which helps in finding affordable and locally available options.\n\n* Cost\n* Mobile apps tend to be more likely to be available for both major mobile operating systems compared to a native desktop-only solution being available on multiple desktop platforms\n* Mobile platform flexibility and global cost and availability compared to pursuing laptops with the same capability\n\n## A working example setup\n\n![](https://about.gitlab.com/images/blogimages/virtualwhiteboarding/whiteboarding-setup-samsung8.jpg)\n\n### Tablet\n\n* My first tablet was a [Samsung Galaxy Tab A 8.0 with Spen (SM-P200)](https://www.amazon.com/gp/product/B07TS2N27S/) that cost me USD $235. It is actually an international edition as the US market does not seem to offer an active pen tablet in the 8” format. In this case, the stylus fits inside the tablet so is really not appropriate for fluid drawing and writing.\n* Since my first purchase, Samsung has come out with a less expensive line of large tablets with active pen technology, so I now also have the [Samsung Galaxy Tab S6 Lite 10.4 inch (SM-P610NZBAXAR)](https://www.amazon.com/SAMSUNG-Android-Included-Speakers-SM-P610NZBAXAR/dp/B086Z3S3MY/), which I obtained for USD $250. While the stylus in this unit is considered more full-sized, the thickness, feel, forefinger button and length all cause me to reach for my after-market stylus for the most fluid experience.\n\n### Stylus\n\n* For a stylus, I use the [STAEDTLER 180 Noris digital classic EMR Stylus](https://www.amazon.com/STAEDTLER-22-1-digital-compatibility-purchase/dp/B0728HBD7F). I find the length, weight, and lack of buttons all helpful in handling drawing and writing smoothly. The color makes it a little less easy to lose. The downsides include that there are very few tablet cases that can accommodate it and that it looks so much like a pencil that someone in your household may accidentally toss it in the regular pen jar. Always be sure to verify stylus compatibility with your device's active pen technology.\n\n### Settings\n\nI almost returned the 10” tablet due to a behavior that a couple settings fixed. Android tablets with no physical buttons put a navigation bar on the bottom part of the screen. I found that I was having my palm be picked up by the Home Screen button, which would close the whiteboarding app. Also, when any of these buttons are activated by the stylus, it is equally disruptive to the flow. For the Navigation Bar settings, I set “Navigation type” to “Swipe gestures” and I enable “Block gestures with S Pen” (Android 12, Samsung OneUI v4.1).\n\n### Collaborative whiteboard options\n\nThere are multiple options for the collaborative whiteboard options - some completely free and paid options that carry enough value-add features to consider it for heavy users.\n\n\n\u003Ctable>\n  \u003Ctr>\n   \u003Ctd>\n   \u003C/td>\n   \u003Ctd>\u003Ca href=\"https://www.liveboard.online\">Liveboard Online\u003C/a>\n   \u003C/td>\n   \u003Ctd>\u003Ca href=\"https://support.google.com/jamboard/answer/7424836?hl=en\">Google Jamboard\u003C/a>\n   \u003C/td>\n   \u003Ctd>\u003Ca href=\"https://miro.com\">Miro\u003C/a>\n   \u003C/td>\n  \u003C/tr>\n  \u003Ctr>\n   \u003Ctd>Long Term Usage of Free Tier\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n   \u003Ctd>No - Only 3 Boards\n   \u003C/td>\n  \u003C/tr>\n  \u003Ctr>\n   \u003Ctd>Native Mobile Apps\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n  \u003C/tr>\n  \u003Ctr>\n   \u003Ctd>Native Desktop Apps\n   \u003C/td>\n   \u003Ctd>No\n   \u003C/td>\n   \u003Ctd>No\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n  \u003C/tr>\n  \u003Ctr>\n   \u003Ctd>Web App (for screen sharing)\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n  \u003C/tr>\n  \u003Ctr>\n   \u003Ctd>Fluidity Extras\n   \u003C/td>\n   \u003Ctd>\n   \u003C/td>\n   \u003Ctd>\n   \u003C/td>\n   \u003Ctd>Multiple pens on deck\n   \u003C/td>\n  \u003C/tr>\n  \u003Ctr>\n   \u003Ctd>Pages / Canvas\n   \u003C/td>\n   \u003Ctd>Pages\n   \u003C/td>\n   \u003Ctd>Pages\n   \u003C/td>\n   \u003Ctd>Endless Canvas\n   \u003C/td>\n  \u003C/tr>\n  \u003Ctr>\n   \u003Ctd>Presentation Mode\n   \u003C/td>\n   \u003Ctd>No\n   \u003C/td>\n   \u003Ctd>No\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n  \u003C/tr>\n  \u003Ctr>\n   \u003Ctd>Direct Import From Google Slides\n   \u003C/td>\n   \u003Ctd>No\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n   \u003Ctd>No\n   \u003C/td>\n  \u003C/tr>\n  \u003Ctr>\n   \u003Ctd>Shape Recognition from hand drawing\n   \u003C/td>\n   \u003Ctd>Light Duty\n   \u003C/td>\n   \u003Ctd>Light Duty\n   \u003C/td>\n   \u003Ctd>Awesome\n   \u003C/td>\n  \u003C/tr>\n  \u003Ctr>\n   \u003Ctd>Reusability of Drawing as Formal Technical Diagrams\n   \u003C/td>\n   \u003Ctd>No\n   \u003C/td>\n   \u003Ctd>No\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n  \u003C/tr>\n  \u003Ctr>\n   \u003Ctd>Viewers Synchronized To Drawing Location\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n   \u003Ctd>Yes\n   \u003C/td>\n   \u003Ctd>Manual\n   \u003C/td>\n  \u003C/tr>\n\u003C/table>\n\nSee here for more [GitLab remote work whiteboarding information](https://handbook.gitlab.com/handbook/company/culture/all-remote/collaboration-and-whiteboarding/).\n\nPhoto by [Kvalifik](https://unsplash.com/@kvalifik?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/s/photos/whiteboard?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)\n",[23,24,25],"solutions architecture","collaboration","remote work","yml",{},true,"/en-us/blog/whiteboarding-remote-work-superpower",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":31,"ogSiteName":32,"ogType":33,"canonicalUrls":31},"https://about.gitlab.com/blog/whiteboarding-remote-work-superpower","https://about.gitlab.com","article","en-us/blog/whiteboarding-remote-work-superpower",[36,24,37],"solutions-architecture","remote-work","lp_fcOqlW7i51P83inF-sxtryYg43MbKnbwRyo1dhjg",{"data":40},{"logo":41,"freeTrial":46,"sales":51,"login":56,"items":61,"search":369,"minimal":400,"duo":419,"switchNav":428,"pricingDeployment":439},{"config":42},{"href":43,"dataGaName":44,"dataGaLocation":45},"/","gitlab logo","header",{"text":47,"config":48},"Get free trial",{"href":49,"dataGaName":50,"dataGaLocation":45},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":52,"config":53},"Talk to sales",{"href":54,"dataGaName":55,"dataGaLocation":45},"/sales/","sales",{"text":57,"config":58},"Sign in",{"href":59,"dataGaName":60,"dataGaLocation":45},"https://gitlab.com/users/sign_in/","sign in",[62,89,184,189,290,350],{"text":63,"config":64,"cards":66},"Platform",{"dataNavLevelOne":65},"platform",[67,73,81],{"title":63,"description":68,"link":69},"The intelligent orchestration platform for DevSecOps",{"text":70,"config":71},"Explore our Platform",{"href":72,"dataGaName":65,"dataGaLocation":45},"/platform/",{"title":74,"description":75,"link":76},"GitLab Duo Agent Platform","Agentic AI for the entire software lifecycle",{"text":77,"config":78},"Meet GitLab Duo",{"href":79,"dataGaName":80,"dataGaLocation":45},"/gitlab-duo-agent-platform/","gitlab duo agent platform",{"title":82,"description":83,"link":84},"Why GitLab","See the top reasons enterprises choose GitLab",{"text":85,"config":86},"Learn more",{"href":87,"dataGaName":88,"dataGaLocation":45},"/why-gitlab/","why gitlab",{"text":90,"left":28,"config":91,"link":93,"lists":97,"footer":166},"Product",{"dataNavLevelOne":92},"solutions",{"text":94,"config":95},"View all Solutions",{"href":96,"dataGaName":92,"dataGaLocation":45},"/solutions/",[98,122,145],{"title":99,"description":100,"link":101,"items":106},"Automation","CI/CD and automation to accelerate deployment",{"config":102},{"icon":103,"href":104,"dataGaName":105,"dataGaLocation":45},"AutomatedCodeAlt","/solutions/delivery-automation/","automated software delivery",[107,111,114,118],{"text":108,"config":109},"CI/CD",{"href":110,"dataGaLocation":45,"dataGaName":108},"/solutions/continuous-integration/",{"text":74,"config":112},{"href":79,"dataGaLocation":45,"dataGaName":113},"gitlab duo agent platform - product menu",{"text":115,"config":116},"Source Code Management",{"href":117,"dataGaLocation":45,"dataGaName":115},"/solutions/source-code-management/",{"text":119,"config":120},"Automated Software Delivery",{"href":104,"dataGaLocation":45,"dataGaName":121},"Automated software delivery",{"title":123,"description":124,"link":125,"items":130},"Security","Deliver code faster without compromising security",{"config":126},{"href":127,"dataGaName":128,"dataGaLocation":45,"icon":129},"/solutions/application-security-testing/","security and compliance","ShieldCheckLight",[131,135,140],{"text":132,"config":133},"Application Security Testing",{"href":127,"dataGaName":134,"dataGaLocation":45},"Application security testing",{"text":136,"config":137},"Software Supply Chain Security",{"href":138,"dataGaLocation":45,"dataGaName":139},"/solutions/supply-chain/","Software supply chain security",{"text":141,"config":142},"Software Compliance",{"href":143,"dataGaName":144,"dataGaLocation":45},"/solutions/software-compliance/","software compliance",{"title":146,"link":147,"items":152},"Measurement",{"config":148},{"icon":149,"href":150,"dataGaName":151,"dataGaLocation":45},"DigitalTransformation","/solutions/visibility-measurement/","visibility and measurement",[153,157,161],{"text":154,"config":155},"Visibility & Measurement",{"href":150,"dataGaLocation":45,"dataGaName":156},"Visibility and Measurement",{"text":158,"config":159},"Value Stream Management",{"href":160,"dataGaLocation":45,"dataGaName":158},"/solutions/value-stream-management/",{"text":162,"config":163},"Analytics & Insights",{"href":164,"dataGaLocation":45,"dataGaName":165},"/solutions/analytics-and-insights/","Analytics and insights",{"title":167,"items":168},"GitLab for",[169,174,179],{"text":170,"config":171},"Enterprise",{"href":172,"dataGaLocation":45,"dataGaName":173},"/enterprise/","enterprise",{"text":175,"config":176},"Small Business",{"href":177,"dataGaLocation":45,"dataGaName":178},"/small-business/","small business",{"text":180,"config":181},"Public Sector",{"href":182,"dataGaLocation":45,"dataGaName":183},"/solutions/public-sector/","public sector",{"text":185,"config":186},"Pricing",{"href":187,"dataGaName":188,"dataGaLocation":45,"dataNavLevelOne":188},"/pricing/","pricing",{"text":190,"config":191,"link":193,"lists":197,"feature":277},"Resources",{"dataNavLevelOne":192},"resources",{"text":194,"config":195},"View all resources",{"href":196,"dataGaName":192,"dataGaLocation":45},"/resources/",[198,231,249],{"title":199,"items":200},"Getting started",[201,206,211,216,221,226],{"text":202,"config":203},"Install",{"href":204,"dataGaName":205,"dataGaLocation":45},"/install/","install",{"text":207,"config":208},"Quick start guides",{"href":209,"dataGaName":210,"dataGaLocation":45},"/get-started/","quick setup checklists",{"text":212,"config":213},"Learn",{"href":214,"dataGaLocation":45,"dataGaName":215},"https://university.gitlab.com/","learn",{"text":217,"config":218},"Product documentation",{"href":219,"dataGaName":220,"dataGaLocation":45},"https://docs.gitlab.com/","product documentation",{"text":222,"config":223},"Best practice videos",{"href":224,"dataGaName":225,"dataGaLocation":45},"/getting-started-videos/","best practice videos",{"text":227,"config":228},"Integrations",{"href":229,"dataGaName":230,"dataGaLocation":45},"/integrations/","integrations",{"title":232,"items":233},"Discover",[234,239,244],{"text":235,"config":236},"Customer success stories",{"href":237,"dataGaName":238,"dataGaLocation":45},"/customers/","customer success stories",{"text":240,"config":241},"Blog",{"href":242,"dataGaName":243,"dataGaLocation":45},"/blog/","blog",{"text":245,"config":246},"Remote",{"href":247,"dataGaName":248,"dataGaLocation":45},"https://handbook.gitlab.com/handbook/company/culture/all-remote/","remote",{"title":250,"items":251},"Connect",[252,257,262,267,272],{"text":253,"config":254},"GitLab Services",{"href":255,"dataGaName":256,"dataGaLocation":45},"/services/","services",{"text":258,"config":259},"Community",{"href":260,"dataGaName":261,"dataGaLocation":45},"/community/","community",{"text":263,"config":264},"Forum",{"href":265,"dataGaName":266,"dataGaLocation":45},"https://forum.gitlab.com/","forum",{"text":268,"config":269},"Events",{"href":270,"dataGaName":271,"dataGaLocation":45},"/events/","events",{"text":273,"config":274},"Partners",{"href":275,"dataGaName":276,"dataGaLocation":45},"/partners/","partners",{"backgroundColor":278,"textColor":279,"text":280,"image":281,"link":285},"#2f2a6b","#fff","Insights for the future of software development",{"altText":282,"config":283},"the source promo card",{"src":284},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758208064/dzl0dbift9xdizyelkk4.svg",{"text":286,"config":287},"Read the latest",{"href":288,"dataGaName":289,"dataGaLocation":45},"/the-source/","the source",{"text":291,"config":292,"lists":294},"Company",{"dataNavLevelOne":293},"company",[295],{"items":296},[297,302,308,310,315,320,325,330,335,340,345],{"text":298,"config":299},"About",{"href":300,"dataGaName":301,"dataGaLocation":45},"/company/","about",{"text":303,"config":304,"footerGa":307},"Jobs",{"href":305,"dataGaName":306,"dataGaLocation":45},"/jobs/","jobs",{"dataGaName":306},{"text":268,"config":309},{"href":270,"dataGaName":271,"dataGaLocation":45},{"text":311,"config":312},"Leadership",{"href":313,"dataGaName":314,"dataGaLocation":45},"/company/team/e-group/","leadership",{"text":316,"config":317},"Team",{"href":318,"dataGaName":319,"dataGaLocation":45},"/company/team/","team",{"text":321,"config":322},"Handbook",{"href":323,"dataGaName":324,"dataGaLocation":45},"https://handbook.gitlab.com/","handbook",{"text":326,"config":327},"Investor relations",{"href":328,"dataGaName":329,"dataGaLocation":45},"https://ir.gitlab.com/","investor relations",{"text":331,"config":332},"Trust Center",{"href":333,"dataGaName":334,"dataGaLocation":45},"/security/","trust center",{"text":336,"config":337},"AI Transparency Center",{"href":338,"dataGaName":339,"dataGaLocation":45},"/ai-transparency-center/","ai transparency center",{"text":341,"config":342},"Newsletter",{"href":343,"dataGaName":344,"dataGaLocation":45},"/company/contact/#contact-forms","newsletter",{"text":346,"config":347},"Press",{"href":348,"dataGaName":349,"dataGaLocation":45},"/press/","press",{"text":351,"config":352,"lists":353},"Contact us",{"dataNavLevelOne":293},[354],{"items":355},[356,359,364],{"text":52,"config":357},{"href":54,"dataGaName":358,"dataGaLocation":45},"talk to sales",{"text":360,"config":361},"Support portal",{"href":362,"dataGaName":363,"dataGaLocation":45},"https://support.gitlab.com","support portal",{"text":365,"config":366},"Customer portal",{"href":367,"dataGaName":368,"dataGaLocation":45},"https://customers.gitlab.com/customers/sign_in/","customer portal",{"close":370,"login":371,"suggestions":378},"Close",{"text":372,"link":373},"To search repositories and projects, login to",{"text":374,"config":375},"gitlab.com",{"href":59,"dataGaName":376,"dataGaLocation":377},"search login","search",{"text":379,"default":380},"Suggestions",[381,383,387,389,393,397],{"text":74,"config":382},{"href":79,"dataGaName":74,"dataGaLocation":377},{"text":384,"config":385},"Code Suggestions (AI)",{"href":386,"dataGaName":384,"dataGaLocation":377},"/solutions/code-suggestions/",{"text":108,"config":388},{"href":110,"dataGaName":108,"dataGaLocation":377},{"text":390,"config":391},"GitLab on AWS",{"href":392,"dataGaName":390,"dataGaLocation":377},"/partners/technology-partners/aws/",{"text":394,"config":395},"GitLab on Google Cloud",{"href":396,"dataGaName":394,"dataGaLocation":377},"/partners/technology-partners/google-cloud-platform/",{"text":398,"config":399},"Why GitLab?",{"href":87,"dataGaName":398,"dataGaLocation":377},{"freeTrial":401,"mobileIcon":406,"desktopIcon":411,"secondaryButton":414},{"text":402,"config":403},"Start free trial",{"href":404,"dataGaName":50,"dataGaLocation":405},"https://gitlab.com/-/trials/new/","nav",{"altText":407,"config":408},"Gitlab Icon",{"src":409,"dataGaName":410,"dataGaLocation":405},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203874/jypbw1jx72aexsoohd7x.svg","gitlab icon",{"altText":407,"config":412},{"src":413,"dataGaName":410,"dataGaLocation":405},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203875/gs4c8p8opsgvflgkswz9.svg",{"text":415,"config":416},"Get Started",{"href":417,"dataGaName":418,"dataGaLocation":405},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com/get-started/","get started",{"freeTrial":420,"mobileIcon":424,"desktopIcon":426},{"text":421,"config":422},"Learn more about GitLab Duo",{"href":79,"dataGaName":423,"dataGaLocation":405},"gitlab duo",{"altText":407,"config":425},{"src":409,"dataGaName":410,"dataGaLocation":405},{"altText":407,"config":427},{"src":413,"dataGaName":410,"dataGaLocation":405},{"button":429,"mobileIcon":434,"desktopIcon":436},{"text":430,"config":431},"/switch",{"href":432,"dataGaName":433,"dataGaLocation":405},"#contact","switch",{"altText":407,"config":435},{"src":409,"dataGaName":410,"dataGaLocation":405},{"altText":407,"config":437},{"src":438,"dataGaName":410,"dataGaLocation":405},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1773335277/ohhpiuoxoldryzrnhfrh.png",{"freeTrial":440,"mobileIcon":445,"desktopIcon":447},{"text":441,"config":442},"Back to pricing",{"href":187,"dataGaName":443,"dataGaLocation":405,"icon":444},"back to pricing","GoBack",{"altText":407,"config":446},{"src":409,"dataGaName":410,"dataGaLocation":405},{"altText":407,"config":448},{"src":413,"dataGaName":410,"dataGaLocation":405},{"title":450,"button":451,"config":456},"See how agentic AI transforms software delivery",{"text":452,"config":453},"Watch GitLab Transcend now",{"href":454,"dataGaName":455,"dataGaLocation":45},"/events/transcend/virtual/","transcend event",{"layout":457,"icon":458,"disabled":28},"release","AiStar",{"data":460},{"text":461,"source":462,"edit":468,"contribute":473,"config":478,"items":483,"minimal":690},"Git is a trademark of Software Freedom Conservancy and our use of 'GitLab' is under license",{"text":463,"config":464},"View page source",{"href":465,"dataGaName":466,"dataGaLocation":467},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/","page source","footer",{"text":469,"config":470},"Edit this page",{"href":471,"dataGaName":472,"dataGaLocation":467},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/content/","web ide",{"text":474,"config":475},"Please contribute",{"href":476,"dataGaName":477,"dataGaLocation":467},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/CONTRIBUTING.md/","please contribute",{"twitter":479,"facebook":480,"youtube":481,"linkedin":482},"https://twitter.com/gitlab","https://www.facebook.com/gitlab","https://www.youtube.com/channel/UCnMGQ8QHMAnVIsI3xJrihhg","https://www.linkedin.com/company/gitlab-com",[484,531,585,629,656],{"title":185,"links":485,"subMenu":500},[486,490,495],{"text":487,"config":488},"View plans",{"href":187,"dataGaName":489,"dataGaLocation":467},"view plans",{"text":491,"config":492},"Why Premium?",{"href":493,"dataGaName":494,"dataGaLocation":467},"/pricing/premium/","why premium",{"text":496,"config":497},"Why Ultimate?",{"href":498,"dataGaName":499,"dataGaLocation":467},"/pricing/ultimate/","why ultimate",[501],{"title":502,"links":503},"Contact Us",[504,507,509,511,516,521,526],{"text":505,"config":506},"Contact sales",{"href":54,"dataGaName":55,"dataGaLocation":467},{"text":360,"config":508},{"href":362,"dataGaName":363,"dataGaLocation":467},{"text":365,"config":510},{"href":367,"dataGaName":368,"dataGaLocation":467},{"text":512,"config":513},"Status",{"href":514,"dataGaName":515,"dataGaLocation":467},"https://status.gitlab.com/","status",{"text":517,"config":518},"Terms of use",{"href":519,"dataGaName":520,"dataGaLocation":467},"/terms/","terms of use",{"text":522,"config":523},"Privacy statement",{"href":524,"dataGaName":525,"dataGaLocation":467},"/privacy/","privacy statement",{"text":527,"config":528},"Cookie preferences",{"dataGaName":529,"dataGaLocation":467,"id":530,"isOneTrustButton":28},"cookie preferences","ot-sdk-btn",{"title":90,"links":532,"subMenu":541},[533,537],{"text":534,"config":535},"DevSecOps platform",{"href":72,"dataGaName":536,"dataGaLocation":467},"devsecops platform",{"text":538,"config":539},"AI-Assisted Development",{"href":79,"dataGaName":540,"dataGaLocation":467},"ai-assisted development",[542],{"title":543,"links":544},"Topics",[545,550,555,560,565,570,575,580],{"text":546,"config":547},"CICD",{"href":548,"dataGaName":549,"dataGaLocation":467},"/topics/ci-cd/","cicd",{"text":551,"config":552},"GitOps",{"href":553,"dataGaName":554,"dataGaLocation":467},"/topics/gitops/","gitops",{"text":556,"config":557},"DevOps",{"href":558,"dataGaName":559,"dataGaLocation":467},"/topics/devops/","devops",{"text":561,"config":562},"Version Control",{"href":563,"dataGaName":564,"dataGaLocation":467},"/topics/version-control/","version control",{"text":566,"config":567},"DevSecOps",{"href":568,"dataGaName":569,"dataGaLocation":467},"/topics/devsecops/","devsecops",{"text":571,"config":572},"Cloud Native",{"href":573,"dataGaName":574,"dataGaLocation":467},"/topics/cloud-native/","cloud native",{"text":576,"config":577},"AI for Coding",{"href":578,"dataGaName":579,"dataGaLocation":467},"/topics/devops/ai-for-coding/","ai for coding",{"text":581,"config":582},"Agentic AI",{"href":583,"dataGaName":584,"dataGaLocation":467},"/topics/agentic-ai/","agentic ai",{"title":586,"links":587},"Solutions",[588,590,592,597,601,604,608,611,613,616,619,624],{"text":132,"config":589},{"href":127,"dataGaName":132,"dataGaLocation":467},{"text":121,"config":591},{"href":104,"dataGaName":105,"dataGaLocation":467},{"text":593,"config":594},"Agile development",{"href":595,"dataGaName":596,"dataGaLocation":467},"/solutions/agile-delivery/","agile delivery",{"text":598,"config":599},"SCM",{"href":117,"dataGaName":600,"dataGaLocation":467},"source code management",{"text":546,"config":602},{"href":110,"dataGaName":603,"dataGaLocation":467},"continuous integration & delivery",{"text":605,"config":606},"Value stream management",{"href":160,"dataGaName":607,"dataGaLocation":467},"value stream management",{"text":551,"config":609},{"href":610,"dataGaName":554,"dataGaLocation":467},"/solutions/gitops/",{"text":170,"config":612},{"href":172,"dataGaName":173,"dataGaLocation":467},{"text":614,"config":615},"Small business",{"href":177,"dataGaName":178,"dataGaLocation":467},{"text":617,"config":618},"Public sector",{"href":182,"dataGaName":183,"dataGaLocation":467},{"text":620,"config":621},"Education",{"href":622,"dataGaName":623,"dataGaLocation":467},"/solutions/education/","education",{"text":625,"config":626},"Financial services",{"href":627,"dataGaName":628,"dataGaLocation":467},"/solutions/finance/","financial services",{"title":190,"links":630},[631,633,635,637,640,642,644,646,648,650,652,654],{"text":202,"config":632},{"href":204,"dataGaName":205,"dataGaLocation":467},{"text":207,"config":634},{"href":209,"dataGaName":210,"dataGaLocation":467},{"text":212,"config":636},{"href":214,"dataGaName":215,"dataGaLocation":467},{"text":217,"config":638},{"href":219,"dataGaName":639,"dataGaLocation":467},"docs",{"text":240,"config":641},{"href":242,"dataGaName":243,"dataGaLocation":467},{"text":235,"config":643},{"href":237,"dataGaName":238,"dataGaLocation":467},{"text":245,"config":645},{"href":247,"dataGaName":248,"dataGaLocation":467},{"text":253,"config":647},{"href":255,"dataGaName":256,"dataGaLocation":467},{"text":258,"config":649},{"href":260,"dataGaName":261,"dataGaLocation":467},{"text":263,"config":651},{"href":265,"dataGaName":266,"dataGaLocation":467},{"text":268,"config":653},{"href":270,"dataGaName":271,"dataGaLocation":467},{"text":273,"config":655},{"href":275,"dataGaName":276,"dataGaLocation":467},{"title":291,"links":657},[658,660,662,664,666,668,670,674,679,681,683,685],{"text":298,"config":659},{"href":300,"dataGaName":293,"dataGaLocation":467},{"text":303,"config":661},{"href":305,"dataGaName":306,"dataGaLocation":467},{"text":311,"config":663},{"href":313,"dataGaName":314,"dataGaLocation":467},{"text":316,"config":665},{"href":318,"dataGaName":319,"dataGaLocation":467},{"text":321,"config":667},{"href":323,"dataGaName":324,"dataGaLocation":467},{"text":326,"config":669},{"href":328,"dataGaName":329,"dataGaLocation":467},{"text":671,"config":672},"Sustainability",{"href":673,"dataGaName":671,"dataGaLocation":467},"/sustainability/",{"text":675,"config":676},"Diversity, inclusion and belonging (DIB)",{"href":677,"dataGaName":678,"dataGaLocation":467},"/diversity-inclusion-belonging/","Diversity, inclusion and belonging",{"text":331,"config":680},{"href":333,"dataGaName":334,"dataGaLocation":467},{"text":341,"config":682},{"href":343,"dataGaName":344,"dataGaLocation":467},{"text":346,"config":684},{"href":348,"dataGaName":349,"dataGaLocation":467},{"text":686,"config":687},"Modern Slavery Transparency Statement",{"href":688,"dataGaName":689,"dataGaLocation":467},"https://handbook.gitlab.com/handbook/legal/modern-slavery-act-transparency-statement/","modern slavery transparency statement",{"items":691},[692,695,698],{"text":693,"config":694},"Terms",{"href":519,"dataGaName":520,"dataGaLocation":467},{"text":696,"config":697},"Cookies",{"dataGaName":529,"dataGaLocation":467,"id":530,"isOneTrustButton":28},{"text":699,"config":700},"Privacy",{"href":524,"dataGaName":525,"dataGaLocation":467},[702],{"id":703,"title":18,"body":8,"config":704,"content":706,"description":8,"extension":26,"meta":712,"navigation":28,"path":713,"seo":714,"stem":715,"__hash__":716},"blogAuthors/en-us/blog/authors/darwin-sanoy.yml",{"template":705},"BlogAuthor",{"role":707,"name":18,"config":708},"Field Chief Cloud Architect",{"headshot":709,"linkedin":710,"ctfId":711},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659751/Blog/Author%20Headshots/Darwin-Sanoy-headshot-395-square-gitlab-teampage-avatar.png","https://linkedin.com/in/darwinsanoy","DarwinJS",{},"/en-us/blog/authors/darwin-sanoy",{},"en-us/blog/authors/darwin-sanoy","UkMMwmU5o2e6Y-wBltA9E_z96LvHuB-bG6VW9DsLzIY",[718,733,746],{"content":719,"config":731},{"body":720,"title":721,"description":722,"authors":723,"heroImage":725,"date":726,"category":9,"tags":727},"Most CI/CD tools can run a build and ship a deployment. Where they diverge is what happens when your delivery needs get real: a monorepo with a dozen services, microservices spread across multiple repositories, deployments to dozens of environments, or a platform team trying to enforce standards without becoming a bottleneck.\n  \nGitLab's pipeline execution model was designed for that complexity. Parent-child pipelines, DAG execution, dynamic pipeline generation, multi-project triggers, merge request pipelines with merged results, and CI/CD Components each solve a distinct class of problems. Because they compose, understanding the full model unlocks something more than a faster pipeline. In this article, you'll learn about the five patterns where that model stands out, each mapped to a real engineering scenario with the configuration to match.\n  \nThe configs below are illustrative. The scripts use echo commands to keep the signal-to-noise ratio low. Swap them out for your actual build, test, and deploy steps and they are ready to use.\n\n\n## 1. Monorepos: Parent-child pipelines + DAG execution\n\n\nThe problem: Your monorepo has a frontend, a backend, and a docs site. Every commit triggers a full rebuild of everything, even when only a README changed.\n\n\nGitLab solves this with two complementary features: [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) (which let a top-level pipeline spawn isolated sub-pipelines) and [DAG execution via `needs`](https://docs.gitlab.com/ci/yaml/#needs) (which breaks rigid stage-by-stage ordering and lets jobs start the moment their dependencies finish).\n\n\nA parent pipeline detects what changed and triggers only the relevant child pipelines:\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - trigger\n\ntrigger-services:\n  stage: trigger\n  trigger:\n    include:\n      - local: '.gitlab/ci/api-service.yml'\n      - local: '.gitlab/ci/web-service.yml'\n      - local: '.gitlab/ci/worker-service.yml'\n    strategy: depend\n```\n\n\nEach child pipeline is a fully independent pipeline with its own stages, jobs, and artifacts. The parent waits for all of them via [strategy: depend](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#wait-for-downstream-pipeline-to-complete) so you get a single green/red signal at the top level, with full drill-down into each service's pipeline. This organizational separation is the bigger win for large teams: each service owns its pipeline config, changes in one cannot break another, and the complexity stays manageable as the repo grows.\n\n\nOne thing worth knowing: when you pass [multiple files to a single `trigger: include:`](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#combine-multiple-child-pipeline-configuration-files), GitLab merges them into a single child pipeline configuration. This means jobs defined across those files share the same pipeline context and can reference each other with `needs:`, which is what makes the DAG optimization possible. If you split them into separate trigger jobs instead, each would be its own isolated pipeline and cross-file `needs:` references would not work.\n\n\nCombine this with `needs:` inside each child pipeline and you get DAG execution. Your integration tests can start the moment the build finishes, without waiting for other jobs in the same stage.\n\n```yaml\n# .gitlab/ci/api-service.yml\nstages:\n  - build\n  - test\n\nbuild-api:\n  stage: build\n  script:\n    - echo \"Building API service\"\n\ntest-api:\n  stage: test\n  needs: [build-api]\n  script:\n    - echo \"Running API tests\"\n```\n\n\nWhy it matters: Teams with large monorepos typically report significant reductions in pipeline runtime after switching to DAG execution, since jobs no longer wait on unrelated work in the same stage. Parent-child pipelines add the organizational layer that keeps the configuration maintainable as the repo and team grow.\n\n![Local downstream pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738759/Blog/Imported/hackathon-fake-blog-post-s/image3_vwj3rz.png \"Local downstream pipelines\")\n\n## 2. Microservices: Cross-repo, multi-project pipelines\n\n\nThe problem: Your frontend lives in one repo, your backend in another. When the frontend team ships a change, they have no visibility into whether it broke the backend integration and vice versa.\n\n\nGitLab's [multi-project pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#multi-project-pipelines) let one project trigger a pipeline in a completely separate project and wait for the result. The triggering project gets a linked downstream pipeline right in its own pipeline view.\n\n\nThe frontend pipeline builds an API contract artifact and publishes it, then triggers the backend pipeline. The backend fetches that artifact directly using the [Jobs API](https://docs.gitlab.com/ee/api/jobs.html#download-a-single-artifact-file-from-specific-tag-or-branch) and validates it before allowing anything to proceed. If a breaking change is detected, the backend pipeline fails and the frontend pipeline fails with it.\n\n```yaml\n# frontend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n  - trigger-backend\n\nbuild-frontend:\n  stage: build\n  script:\n    - echo \"Building frontend and generating API contract...\"\n    - mkdir -p dist\n    - |\n      echo '{\n        \"api_version\": \"v2\",\n        \"breaking_changes\": false\n      }' > dist/api-contract.json\n    - cat dist/api-contract.json\n  artifacts:\n    paths:\n      - dist/api-contract.json\n    expire_in: 1 hour\n\ntest-frontend:\n  stage: test\n  script:\n    - echo \"All frontend tests passed!\"\n\ntrigger-backend-pipeline:\n  stage: trigger-backend\n  trigger:\n    project: my-org/backend-service\n    branch: main\n    strategy: depend\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n```\n\n```yaml\n# backend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n\nbuild-backend:\n  stage: build\n  script:\n    - echo \"All backend tests passed!\"\n\nintegration-test:\n  stage: test\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"pipeline\"\n  script:\n    - echo \"Fetching API contract from frontend...\"\n    - |\n      curl --silent --fail \\\n        --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n        --output api-contract.json \\\n        \"${CI_API_V4_URL}/projects/${FRONTEND_PROJECT_ID}/jobs/artifacts/main/raw/dist/api-contract.json?job=build-frontend\"\n    - cat api-contract.json\n    - |\n      if grep -q '\"breaking_changes\": true' api-contract.json; then\n        echo \"FAIL: Breaking API changes detected - backend integration blocked!\"\n        exit 1\n      fi\n      echo \"PASS: API contract is compatible!\"\n```\n\n\nA few things worth noting in this config. The `integration-test` job uses `$CI_PIPELINE_SOURCE == \"pipeline\"` to ensure it only runs when triggered by an upstream pipeline, not on a standalone push to the backend repo. The frontend project ID is referenced via `$FRONTEND_PROJECT_ID`, which should be set as a [CI/CD variable](https://docs.gitlab.com/ci/variables/) in the backend project settings to avoid hardcoding it.\n\n\nWhy it matters: Cross-service breakage that previously surfaced in production gets caught in the pipeline instead. The dependency between services stops being invisible and becomes something teams can see, track, and act on.\n\n\n![Cross-project pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738762/Blog/Imported/hackathon-fake-blog-post-s/image4_h6mfsb.png \"Cross-project pipelines\")\n\n\n## 3. Multi-tenant / matrix deployments: Dynamic child pipelines\n\n\nThe problem: You deploy the same application to 15 customer environments, or three cloud regions, or dev/staging/prod. Updating a deploy stage across all of them one by one is the kind of work that leads to configuration drift. Writing a separate pipeline for each environment is unmaintainable from day one.\n\n\nGitLab's [dynamic child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#dynamic-child-pipelines) let you generate a pipeline at runtime. A job runs a script that produces a YAML file, and that YAML becomes the pipeline for the next stage. The pipeline structure itself becomes data.\n\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - generate\n  - trigger-environments\n\ngenerate-config:\n  stage: generate\n  script:\n    - |\n      # ENVIRONMENTS can be passed as a CI variable or read from a config file.\n      # Default to dev, staging, prod if not set.\n      ENVIRONMENTS=${ENVIRONMENTS:-\"dev staging prod\"}\n      for ENV in $ENVIRONMENTS; do\n        cat > ${ENV}-pipeline.yml \u003C\u003C EOF\n      stages:\n        - deploy\n        - verify\n      deploy-${ENV}:\n        stage: deploy\n        script:\n          - echo \"Deploying to ${ENV} environment\"\n      verify-${ENV}:\n        stage: verify\n        script:\n          - echo \"Running smoke tests on ${ENV}\"\n      EOF\n      done\n  artifacts:\n    paths:\n      - \"*.yml\"\n    exclude:\n      - \".gitlab-ci.yml\"\n\n.trigger-template:\n  stage: trigger-environments\n  trigger:\n    strategy: depend\n\ntrigger-dev:\n  extends: .trigger-template\n  trigger:\n    include:\n      - artifact: dev-pipeline.yml\n        job: generate-config\n\ntrigger-staging:\n  extends: .trigger-template\n  needs: [trigger-dev]\n  trigger:\n    include:\n      - artifact: staging-pipeline.yml\n        job: generate-config\n\ntrigger-prod:\n  extends: .trigger-template\n  needs: [trigger-staging]\n  trigger:\n    include:\n      - artifact: prod-pipeline.yml\n        job: generate-config\n  when: manual\n```\n\n\nThe generation script loops over an `ENVIRONMENTS` variable rather than hardcoding each environment separately. Pass in a different list via a CI variable or read it from a config file and the pipeline adapts without touching the YAML. The trigger jobs use [extends:](https://docs.gitlab.com/ci/yaml/#extends) to inherit shared configuration from `.trigger-template`, so `strategy: depend` is defined once rather than repeated on every trigger job. Add a new environment by updating the variable, not by duplicating pipeline config. Add [when: manual](https://docs.gitlab.com/ci/yaml/#when) to the production trigger and you get a promotion gate baked right into the pipeline graph.\n\n\nWhy it matters: SaaS companies and platform teams use this pattern to manage dozens of environments without duplicating pipeline logic. The pipeline structure itself stays lean as the deployment matrix grows.\n\n\n![Dynamic pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738765/Blog/Imported/hackathon-fake-blog-post-s/image7_wr0kx2.png \"Dynamic pipeline\")\n\n\n## 4. MR-first delivery: Merge request pipelines, merged results, and workflow routing\n\n\nThe problem: Your pipeline runs on every push to every branch. Expensive tests run on feature branches that will never merge. Meanwhile, you have no guarantee that what you tested is actually what will land on `main` after a merge.\n\n\nGitLab has three interlocking features that solve this together:\n\n\n*   [Merge request pipelines](https://docs.gitlab.com/ci/pipelines/merge_request_pipelines/) run only when a merge request exists, not on every branch push. This alone eliminates a significant amount of wasted compute.\n\n*   [Merged results pipelines](https://docs.gitlab.com/ci/pipelines/merged_results_pipelines/) go further. GitLab creates a temporary merge commit (your branch plus the current target branch) and runs the pipeline against that. You are testing what will actually exist after the merge, not just your branch in isolation.\n\n*   [Workflow rules](https://docs.gitlab.com/ci/yaml/workflow/) let you define exactly which pipeline type runs under which conditions and suppress everything else. The `$CI_OPEN_MERGE_REQUESTS` guard below prevents duplicate pipelines firing for both a branch and its open MR simultaneously.\n\n\nWith those three working together, here is what a tiered pipeline looks like:\n\n```yaml\n# .gitlab-ci.yml\nworkflow:\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n      when: never\n    - if: $CI_COMMIT_BRANCH\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\nstages:\n  - fast-checks\n  - expensive-tests\n  - deploy\n\nlint-code:\n  stage: fast-checks\n  script:\n    - echo \"Running linter\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nunit-tests:\n  stage: fast-checks\n  script:\n    - echo \"Running unit tests\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nintegration-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running integration tests (15 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\ne2e-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running E2E tests (30 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nnightly-comprehensive-scan:\n  stage: expensive-tests\n  script:\n    - echo \"Running full nightly suite (2 hours)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\ndeploy-production:\n  stage: deploy\n  script:\n    - echo \"Deploying to production\"\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n      when: manual\n```\n\nWith this setup, the pipeline behaves differently depending on context. A push to a feature branch with no open MR runs lint and unit tests only. Once an MR is opened, the workflow rules switch from a branch pipeline to an MR pipeline, and the full integration and E2E suite runs against the merged result. Merging to `main` queues a manual production deployment. A nightly schedule runs the comprehensive scan once, not on every commit.\n\n\nWhy it matters: Teams routinely cut CI costs significantly with this pattern, not by running fewer tests, but by running the right tests at the right time. Merged results pipelines catch the class of bugs that only appear after a merge, before they ever reach `main`.\n\n\n![Conditional pipelines (within a branch with no MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738768/Blog/Imported/hackathon-fake-blog-post-s/image6_dnfcny.png \"Conditional pipelines (within a branch with no MR)\")\n\n\n\n![Conditional pipelines (within an MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738772/Blog/Imported/hackathon-fake-blog-post-s/image1_wyiafu.png \"Conditional pipelines (within an MR)\")\n\n\n\n![Conditional pipelines (on the main branch)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738774/Blog/Imported/hackathon-fake-blog-post-s/image5_r6lkfd.png \"Conditional pipelines (on the main branch)\")\n\n## 5. Governed pipelines: CI/CD Components\n\n\nThe problem: Your platform team has defined the right way to build, test, and deploy. But every team has their own `.gitlab-ci.yml` with subtle variations. Security scanning gets skipped. Deployment standards drift. Audits are painful.\n\n\nGitLab [CI/CD Components](https://docs.gitlab.com/ci/components/) let platform teams publish versioned, reusable pipeline building blocks. Application teams consume them with a single `include:` line and optional inputs — no copy-paste, no drift. Components are discoverable through the [CI/CD Catalog](https://docs.gitlab.com/ci/components/#cicd-catalog), which means teams can find and adopt approved building blocks without needing to go through the platform team directly.\n\n\nHere is a component definition from a shared library:\n\n```yaml\n# templates/deploy.yml\nspec:\n  inputs:\n    stage:\n      default: deploy\n    environment:\n      default: production\n---\ndeploy-job:\n  stage: $[[ inputs.stage ]]\n  script:\n    - echo \"Deploying $APP_NAME to $[[ inputs.environment ]]\"\n    - echo \"Deploy URL: $DEPLOY_URL\"\n  environment:\n    name: $[[ inputs.environment ]]\n```\nAnd here is how an application team consumes it:\n\n```yaml\n# Application repo: .gitlab-ci.yml\nvariables:\n  APP_NAME: \"my-awesome-app\"\n  DEPLOY_URL: \"https://api.example.com\"\n\ninclude:\n  - component: gitlab.com/my-org/component-library/build@v1.0.6\n  - component: gitlab.com/my-org/component-library/test@v1.0.6\n  - component: gitlab.com/my-org/component-library/deploy@v1.0.6\n    inputs:\n      environment: staging\n\nstages:\n  - build\n  - test\n  - deploy\n```\n\nThree lines of `include:` replace hundreds of lines of duplicated YAML. The platform team can push a security fix to `v1.0.7` and teams opt in on their own schedule — or the platform team can pin everyone to a minimum version. Either way, one change propagates everywhere instead of needing to be applied repo by repo.\n\n\nPair this with [resource groups](https://docs.gitlab.com/ci/resource_groups/) to prevent concurrent deployments to the same environment, and [protected environments](https://docs.gitlab.com/ci/environments/protected_environments/) to enforce approval gates - and you have a governed delivery platform where compliance is the default, not the exception.\n\n\nWhy it matters: This is the pattern that makes GitLab CI/CD scale across hundreds of teams. Platform engineering teams enforce compliance without becoming a bottleneck. Application teams get a fast path to a working pipeline without reinventing the wheel.\n\n\n![Component pipeline (imported jobs)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738776/Blog/Imported/hackathon-fake-blog-post-s/image2_pizuxd.png \"Component pipeline (imported jobs)\")\n\n## Putting it all together\n\nNone of these features exist in isolation. The reason GitLab's pipeline model is worth understanding deeply is that these primitives compose:\n\n*   A monorepo uses parent-child pipelines, and each child uses DAG execution\n\n*   A microservices platform uses multi-project pipelines, and each project uses MR pipelines with merged results\n\n*   A governed platform uses CI/CD components to standardize the patterns above across every team\n\n\nMost teams discover one of these features when they hit a specific pain point. The ones who invest in understanding the full model end up with a delivery system that actually reflects how their engineering organization works, not a pipeline that fights it.\n\n## Other patterns worth exploring\n\n\nThe five patterns above cover the most common structural pain points, but GitLab's pipeline model goes further. A few others worth looking into as your needs grow:\n\n\n*   [Review apps with dynamic environments](https://docs.gitlab.com/ci/environments/) let you spin up a live preview for every feature branch and tear it down automatically when the MR closes. Useful for teams doing frontend work or API changes that need stakeholder sign-off before merging.\n\n*   [Caching and artifact strategies](https://docs.gitlab.com/ci/caching/) are often the fastest way to cut pipeline runtime after the structural work is done. Structuring `cache:` keys around dependency lockfiles and being deliberate about what gets passed between jobs with [artifacts:](https://docs.gitlab.com/ci/yaml/#artifacts) can make a significant difference without changing your pipeline shape at all.\n\n*   [Scheduled and API-triggered pipelines](https://docs.gitlab.com/ci/pipelines/schedules/) are worth knowing about because not everything should run on a code push. Nightly security scans, compliance reports, and release automation are better modeled as scheduled or [API-triggered](https://docs.gitlab.com/ci/triggers/) pipelines with `$CI_PIPELINE_SOURCE` routing the right jobs for each context.\n\n## How to get started\n\nModern software delivery is complex. Teams are managing monorepos with dozens of services, coordinating across multiple repositories, deploying to many environments at once, and trying to keep standards consistent as organizations grow. GitLab's pipeline model was built with all of that in mind.\n\nWhat makes it worth investing time in is how well the pieces fit together. Parent-child pipelines bring structure to large codebases. Multi-project pipelines make cross-team dependencies visible and testable. Dynamic pipelines turn environment management into something that scales gracefully. MR-first delivery with merged results ensures confidence at every step of the review process. And CI/CD Components give platform teams a way to share best practices across an entire organization without becoming a bottleneck.\n\nEach of these features is powerful on its own, and even more so when combined. GitLab gives you the building blocks to design a delivery system that fits how your team actually works, and grows with you as your needs evolve.\n\n> [Start a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/) to use pipeline logic today.\n\n## Read more\n\n*   [Variable and artifact sharing in GitLab parent-child pipelines](https://about.gitlab.com/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines/)\n*   [CI/CD inputs: Secure and preferred method to pass parameters to a pipeline](https://about.gitlab.com/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline/)\n*   [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n*   [How to include file references in your CI/CD components](https://about.gitlab.com/blog/how-to-include-file-references-in-your-ci-cd-components/)\n*   [FAQ: GitLab CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/)\n*   [Building a GitLab CI/CD pipeline for a monorepo the easy way](https://about.gitlab.com/blog/building-a-gitlab-ci-cd-pipeline-for-a-monorepo-the-easy-way/)\n*   [A CI/CD component builder's journey](https://about.gitlab.com/blog/a-ci-component-builders-journey/)\n*   [CI/CD Catalog goes GA: No more building pipelines from scratch](https://about.gitlab.com/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch/)","5 ways GitLab pipeline logic solves real engineering problems","Learn how to scale CI/CD with composable patterns for monorepos, microservices, environments, and governance.",[724],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[108,728,729,730],"DevOps platform","tutorial","features",{"featured":28,"template":13,"slug":732},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":734,"config":744},{"title":735,"description":736,"authors":737,"heroImage":739,"date":740,"body":741,"category":9,"tags":742},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[738],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n  \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n  gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n  1. Do I have this cached? → Return it\n  2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│                    CI/CD Pipeline                       │\n│                          │                              │\n│                          ▼                              │\n│   gitlab.com/virtual_registries/container/\u003Cid>/image   │\n└─────────────────────────────────────────────────────────┘\n                           │\n                           ▼\n┌─────────────────────────────────────────────────────────┐\n│            Container Virtual Registry                   │\n│                                                         │\n│  Upstream 1: Docker Hub ────────────────┐               │\n│  Upstream 2: dhi.io (Hardened) ────────┐│               │\n│  Upstream 3: MCR ─────────────────────┐││               │\n│  Upstream 4: Quay.io ────────────────┐│││               │\n│                                      ││││               │\n│                    ┌─────────────────┴┴┴┴──┐            │\n│                    │        Cache          │            │\n│                    │  (manifests + layers) │            │\n│                    └───────────────────────┘            │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficult to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n    group_id=\"785414\",  # Your top-level group ID\n    name=\"platform-images\",\n    description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://registry-1.docker.io\",\n    name=\"Docker Hub\",\n    cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-docker-username\",\n    password=\"your-docker-access-token\",\n    cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://mcr.microsoft.com\",\n    name=\"Microsoft Container Registry\",\n    cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://quay.io\",\n    name=\"Quay.io\",\n    cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n  VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n  \nbuild:\n  image: docker:24\n  services:\n    - docker:24-dind\n  before_script:\n    # Authenticate to GitLab (which handles upstream auth for you)\n    - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n  script:\n    # All of these go through your single virtual registry\n    \n    # Official Docker Hub images (use library/ prefix)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n    \n    # Docker Hardened Images from dhi.io (no prefix needed)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n    \n    # .NET from MCR\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n    entries = client.list_cache_entries(upstream['id'])\n    print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-username\",\n    password=\"your-token\",\n    cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[729,743,730],"product",{"featured":12,"template":13,"slug":745},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":747,"config":757},{"title":748,"description":749,"authors":750,"heroImage":752,"date":753,"category":9,"tags":754,"body":756},"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.",[751],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[261,623,755],"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-agent-platform/) 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":758,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":760},[761,775,786,798],{"id":762,"categories":763,"header":765,"text":766,"button":767,"image":772},"ai-modernization",[764],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":768,"config":769},"Get your AI maturity score",{"href":770,"dataGaName":771,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":773},{"src":774},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":776,"categories":777,"header":778,"text":766,"button":779,"image":783},"devops-modernization",[743,569],"Are you just managing tools or shipping innovation?",{"text":780,"config":781},"Get your DevOps maturity score",{"href":782,"dataGaName":771,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":784},{"src":785},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":787,"categories":788,"header":790,"text":766,"button":791,"image":795},"security-modernization",[789],"security","Are you trading speed for security?",{"text":792,"config":793},"Get your security maturity score",{"href":794,"dataGaName":771,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":796},{"src":797},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":799,"paths":800,"header":803,"text":804,"button":805,"image":810},"github-azure-migration",[801,802],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":806,"config":807},"See how GitLab compares to GitHub",{"href":808,"dataGaName":809,"dataGaLocation":243},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":811},{"src":785},{"header":813,"blurb":814,"button":815,"secondaryButton":820},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":816,"config":817},"Get your free trial",{"href":818,"dataGaName":50,"dataGaLocation":819},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":505,"config":821},{"href":54,"dataGaName":55,"dataGaLocation":819},1776449939877]