cloudscoop’s Post

🚀 Auto Deploy Static Website using AWS Amplify + GitHub This time, I’ve taken it one step further 🔥 Instead of manually uploading files to S3, I demonstrated how to: 👉 Push code to GitHub 👉 Connect it with AWS Amplify 👉 Enable automatic deployment on every code update 🔗 GitHub Repository (Code + Guide): https://lnkd.in/g_mcUKtf 💡 What’s special in this setup? Fully automated CI/CD pipeline ⚡ No manual upload required Every push → Auto build → Auto deploy Real-world production workflow used by companies 🧠 Tech Stack Used: Amazon Web Services AWS Amplify GitHub 📌 What you’ll learn: ✔ Connecting GitHub repo to Amplify ✔ Setting up auto-deployment pipeline ✔ Build & deploy configuration ✔ Continuous integration basics ✔ Production-level hosting workflow 📈 Why this is important? In real companies, developers don’t upload files manually. They push code → pipeline handles everything automatically. 🙏 Special thanks to Ulhas Narwade (Cloud Messenger☁️📨) Sir and Amazon Web Services (AWS) for continuous guidance. Saroj Kumar Chand Rashmi Bhakre Ashlesha Athale Sudarshan Darade Akash kolhe alhad prabhudesai Vasant Mane Ravikala Zilte Madhan G Jaleel Shaik If you want to become a Cloud / DevOps Engineer, this is a MUST skill 💯 💬 Drop your thoughts & let’s connect! #AWS #AWSAmplify #GitHub #CICD #DevOps #CloudComputing #Automation #WebDevelopment #Frontend #Deployment #CloudEngineer #TechIndia #Learning #SoftwareEngineering #BuildInPublic

🚀 Built my first CI pipeline using Azure DevOps! CI/CD is a critical skill for modern developers, and I’ve started implementing it hands-on as part of my backend journey. Excited to share that I’ve successfully designed and executed a CI pipeline using Azure DevOps 🎯 🔧 What I worked on: • Created and managed a repository in Azure Repos • Configured a YAML-based pipeline for automation • Set up a service connection for Azure Function App integration • Created and configured a build agent for pipeline execution • Automated the build process for a Node.js application • Enabled pipeline triggers on every code push ⚙️ Tech Stack: • Azure DevOps (Pipelines) • Node.js • YAML 💡 Key Learning: Hands-on experience with CI pipelines helped me understand how automation improves development speed, consistency, and reliability in real-world applications. 📌 What’s Next: Planning to implement full CI/CD by integrating deployment to Azure Functions and building an end-to-end serverless pipeline 🚀 #AzureDevOps #CICD #DevOps #NodeJS #Azure #BackendDeveloper #CloudComputing #LearningInPublic

🚀 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐀𝐖𝐒 𝐂𝐃𝐊 – 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐚𝐬 𝐂𝐨𝐝𝐞 In today’s cloud-driven world, managing infrastructure efficiently is just as important as writing application code. This is where the AWS Cloud Development Kit (CDK) comes into play. 💡 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗔𝗪𝗦 𝗖𝗗𝗞? AWS CDK is an open-source software development framework that allows you to define cloud infrastructure using familiar programming languages like TypeScript, Python, Java, and C#. Instead of writing long YAML/JSON templates, you write clean, reusable code. 🔥 𝗪𝗵𝘆 𝗖𝗗𝗞? ✅ Developer-Friendly – Write infrastructure using real programming languages instead of complex templates ✅ Reusable Components – Create constructs and reuse them across projects ✅ Faster Development – Less boilerplate, more productivity ✅ Power of Abstraction – High-level constructs simplify complex AWS services ✅ Seamless Integration – Works smoothly with the AWS ecosystem ⚡ 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝗖𝗗𝗞 ✨ Less Code, More Power – No more lengthy CloudFormation templates ✨ Strong Typing & IDE Support – Catch errors early with autocomplete & type checking ✨ Reusable Constructs – Build once, use everywhere ✨ Easier Maintenance – Code is easier to read and manage ✨ Supports Modern Dev Practices – CI/CD friendly ⚠️ 𝗗𝗶𝘀𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝗖𝗗𝗞 ❌ Learning Curve – Requires understanding both AWS & programming concepts ❌ Abstraction Complexity – Debugging can sometimes be tricky ❌ Dependency on CloudFormation – Still relies on CloudFormation under the hood 🎯 𝗪𝗵𝗲𝗻 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗨𝘀𝗲 𝗖𝗗𝗞? 👉 When you prefer coding over writing templates 👉 When building complex, scalable cloud architectures 👉 When you want reusable infrastructure patterns 👉 When working in DevOps-driven environments 💬𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝘀 AWS CDK is not just a tool—it’s a shift towards treating infrastructure like real software. If you're a DevOps engineer or cloud developer, mastering CDK can significantly boost your productivity and scalability. 🔥 Are you using CDK in your projects? What has your experience been like? Let’s discuss 👇 #AWS #CDK #DevOps NiCE Amazon Web Services (AWS) Amazon #CloudComputing #InfrastructureAsCode #Programming #Cloud #Tech #CloudComputing #ContinuousLearning #Automation

Build Your AWS CI/CD Pipeline in 5 Simple Steps Automating your deployment process is essential for modern applications. With AWS CI/CD services, you can build, test, and deploy your code quickly and reliably. Here’s a simple 5-step process to build an AWS pipeline: 1. Source – Store your code in repositories like GitHub, AWS CodeCommit, or Bitbucket. 2. Build – Use AWS CodeBuild to compile code, run tests, and create build artifacts. 3. Artifact Storage – Store build outputs in Amazon S3. 4. Deploy – Deploy applications using AWS CodeDeploy or container services like ECS/EKS. 5. Pipeline Automation – Orchestrate the entire workflow using AWS CodePipeline. This setup enables continuous integration and continuous delivery (CI/CD), helping teams release features faster with fewer manual steps. Learning cloud deployment and DevOps pipelines is a must-have skill for modern developers. Learn how to create an ETL pipeline using Glue with Python scripting and the AWS Glue UI! #AWS #DevOps #CICD #CloudComputing #AWSCodePipeline #AWSCodeBuild #AWSCodeDeploy #SoftwareEngineering #WebDevelopment #TechLearning

𝐑𝐞𝐥𝐞𝐚𝐬𝐞 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐢𝐬𝐬𝐮𝐞 𝐬𝐢𝐝𝐞𝐛𝐚𝐫 𝐚𝐧𝐝 𝐝𝐞𝐟𝐚𝐮𝐥𝐭 𝐯𝐚𝐥𝐮𝐞𝐬 𝐟𝐨𝐫 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐟𝐢𝐞𝐥𝐝𝐬 This GitHub update integrates release tracking directly within the issue sidebar and provides default values for project fields. This enhances issue management and speeds up navigation between related issues. 💡 Act now to take advantage of the new release tracking features, as they improve efficiency in issue management and enhance team collaboration. 👉 https://lnkd.in/edifEMsC GITHUB — GitHub · 🟡 MEDIUM #AWS #AmazonWebServices #CloudComputing #DevOps #CloudUpdates

🚀 Day 14 is LIVE – AWS CodeBuild Mastery! Continuing my DevOps Series, today I explored one of the most important CI tools in AWS — AWS CodeBuild. In this hands-on session, I implemented a complete CI workflow and tackled real-world issues like: ✔️ Setting up CodeBuild with CodeCommit ✔️ Writing production-grade buildspec.yml ✔️ Debugging IAM and authorization errors ✔️ Fixing build failures (package.json, runtime issues) ✔️ Understanding how CI works in real DevOps environments This wasn’t just theory — it was full practical troubleshooting and real pipeline execution. 💡 Key takeaway: A DevOps engineer is not someone who just runs tools, but someone who can debug and fix issues under pressure. Day by day, I’m building real DevOps skills and sharing the journey. 🎯 Day 14: AWS CodeBuild – CI Pipeline in Action 👇 Video link in comments #DevOps #AWS #CodeBuild #CICD #CloudComputing #LearningInPublic #TechWithDiwana #DevOpsJourney

AWS is streamlining the developer experience with new "Design-first" and "Bugfix" workflows for Kiro. Faster deployments and automated fixes are now just a click away. #AWS #CloudComputing #DevOps #TechNews

🚀 New Project: Multi-Environment Terraform Deployment with GitLab CI/CD One thing every DevOps engineer encounters early on: how do you manage dev, staging, and prod infrastructure without duplicating code or risking state conflicts? Here's what I built to solve exactly that What the project does: A fully automated IaC pipeline that provisions isolated AWS environments (develop + prod) from a single Terraform codebase, triggered automatically by GitLab CI/CD on every push. How it works: → Push to develop → pipeline runs → staging EC2 deployed (manual approval required) → Merge to main → pipeline runs → prod EC2 deployed (automatic) → Each environment gets its own isolated Terraform state in S3 → State locking prevents concurrent pipeline runs from corrupting infrastructure Stack: • Terraform Workspaces: one codebase, multiple isolated environments • AWS S3: remote backend for shared, versioned state storage • GitLab CI/CD: 3-stage pipeline: validate → plan → apply • AWS EC2 + Security Groups: environment-tagged resources • IAM: least-privilege service account for the pipeline Key lessons learned: • TF_WORKSPACE is a reserved Terraform variable, naming your CI variable the same breaks workspace selection silently (fun one to debug 🙃) • GitLab Protected variables are only injected into protected branches, unprotect them if your pipeline runs on feature/develop branches • Terraform 1.10+ native S3 locking (use_lockfile) replaces the DynamoDB dependency, simpler and cleaner • Manual approval gates in CI aren't just a safety net, they're standard practice in real teams Why this matters for interviews: Remote state, workspace isolation, and branch-based deployment strategies are questions I now get asked about, and can answer from real hands-on experience, not just theory. Full project with README guide on GitHub: https://lnkd.in/dgNT_NTe #DevOps #Terraform #GitLabCI #AWS #InfrastructureAsCode #CloudEngineering #IaC #Berlin #OpenToWork

AWS DevOps Agent launched at re:Invent back in December, but I was juggling a million other things that week, so I never got time to dig in and figure it out even though it seemed cool. It went GA last week, so I finally went through the interactive demo on the product page. I expected the usual polished walkthrough where everything works perfectly on a toy example. Instead I watched it investigate a Lambda function with a 100% error rate. In about ten minutes, it: - Found the deployed bundle was missing a module - Traced it to a specific commit (with the author, the date, the exact lines of code) - Explained why esbuild didn't catch it at build time (the code used a dynamic import pattern that bypasses static analysis) - Wrote a mitigation plan with the actual AWS CLI commands, using the real function name and region, plus rollback steps - Generated a spec for the permanent code fix that you can hand to Kiro or another coding agent The build system explanation is what got me. The agent worked out that esbuild couldn't statically analyse the dynamic import, so CDK deployed successfully while the missing module only surfaced at runtime. That's the kind of thing that takes me a couple of hours to piece together from CloudWatch logs, git blame, and build configs. It's read-only at GA (investigates and recommends, doesn't take actions in your environment). Pricing is $0.0083/second of active work, so this ten-minute investigation would have cost about $5, but also there's a 2-month free trial. I wrote up the full walkthrough with screenshots on my blog https://lnkd.in/eiZXVyxz What does your incident investigation process look like right now? I'm curious how long a typical root cause analysis takes your team 👇 👩🏻💻Follow me (Brooke Jamieson) to stay in the loop with the latest AWS + AI updates for developers 📍 save + share! 🏷️ Amazon Web Services (AWS) AWS Developers #AWS #DevOps #SRE #IncidentResponse #AWSDevOpsAgent 🐈 This is a clear and conspicuous disclaimer that I am an AWS employee and all opinions are my own 🐈

From Zero to Production — My 12-Week DevOps Journey 🚀 Over the past 12 weeks, with the guidance and full support of Oluwatobi Ogundimu, I built a full-stack application from scratch and deployed it end-to-end using industry-standard DevOps tools. 📦 Tech Stack: ☁️ Azure — Cloud infrastructure 🏗️ Terraform — Infrastructure as Code (VMs, VNet, NSG, Public IP) 🐙 GitHub — Source code & version control ⚙️ Jenkins — CI/CD automation 🐳 Docker — Containerization 🗄️ Docker Hub — Image registry ☸️ Kubernetes — Container orchestration 🔁 CI/CD Pipeline in Action: Push code to GitHub Jenkins triggers: build → test → deploy Docker images Docker images pushed to Docker Hub Kubernetes updates deployments automatically Rolling updates with zero downtime 💡 Key Learnings: Decoupled frontend & backend into independent services Mastered Kubernetes container management Leveraged Infrastructure as Code for repeatable deployments Debugging Jenkins failures and connecting the full stack was challenging but rewarding — this is where real growth happens! 💪 Check out the project on GitHub 🔗https://lnkd.in/dXb_-fD4 #DevOps #CloudEngineering #Kubernetes #Docker #Jenkins #Terraform #Azure #CI_CD #LearningInPublic