Azure DevOps Project with Terraform Kubernetes and GitHub Actions

🚀 Recently worked on building a secure CI/CD pipeline on AWS as part of my DevOps learning journey. I wanted to go beyond basic deployments and understand how real production environments handle automation + security together, so I designed a small end-to-end project from scratch. 💻 What I built • GitHub push automatically triggers deployment • Infrastructure created using Terraform (IaC) • Terraform state stored remotely in Amazon S3 instead of local files • GitHub Actions securely connects to AWS using OIDC (no access keys stored) • Application deployed automatically to Amazon EKS (Kubernetes) ⚙️ Tools & Technologies Terraform | GitHub Actions | AWS IAM | OIDC | Amazon EKS | Kubernetes | kubectl | S3 Backend 🔐 One thing I specifically focused on was security. Instead of keeping AWS credentials inside GitHub secrets, the pipeline assumes an IAM role using OIDC and generates temporary credentials during runtime. This helped me understand how modern DevOps teams avoid long-term credential exposure. ☁️ Terraform Learning Earlier I used local .tfstate files, but in this project I configured a remote backend in S3, which made me understand: ✔ centralized state management ✔ safer collaboration ✔ consistent infrastructure provisioning 💡 Key Takeaways Infrastructure should be reproducible, not manual Security should be part of CI/CD design, not an afterthought Small projects teach big real-world concepts Still learning and exploring more in DevOps & Cloud ☁️🚀 #DevOpsJourney #Terraform #GitHubActions #AWS #Kubernetes #EKS #OIDC #CICD #InfrastructureAsCode

🚀 From Code to Cloud: My DevOps CI/CD Pipeline Architecture As part of my DevOps learning journey, I designed a complete CI/CD pipeline using AWS and modern DevOps tools. 🔧 Tech Stack Used: GitHub (Source Code) Jenkins (CI/CD Automation) Docker (Containerization) Kubernetes (Orchestration) Terraform (Infrastructure as Code) AWS (Cloud Platform) 📌 Workflow: 1️⃣ Developer pushes code to GitHub 2️⃣ Jenkins triggers build pipeline 3️⃣ Docker image is created 4️⃣ Image pushed to Docker Hub / AWS ECR 5️⃣ Kubernetes deploys the application 6️⃣ Terraform manages infrastructure 💡 This architecture helps in: Faster deployments 🚀 Scalability 📈 Automation ⚙️ Reduced manual errors ❌ 📊 Sharing my architecture diagram below 👇 #DevOps #AWS #CloudComputing #Docker #Kubernetes #Terraform #CICD #Jenkins #LearningInPublic

🚀 Built a Production-Ready Terraform Project on AWS (Real DevOps Implementation) As part of strengthening my DevOps expertise, I designed and deployed a modular Terraform project to provision AWS infrastructure — following real-world practices used in organizations. Instead of writing everything in a single file, I implemented a scalable and reusable architecture using Terraform modules 👇 🏗️ What I built: ✔️ VPC with public subnet ✔️ Internet Gateway & Route Table configuration ✔️ Security Group (SSH & HTTP access) ✔️ EC2 instance deployment 📁 Project Approach (Industry-Level): 🔹 Separate modules for VPC and EC2 🔹 Environment-based structure (dev) 🔹 Clean and maintainable code design 💡 Real Challenges I Solved (Hands-on Debugging): 🔸 Fixed invalid AMI issue (region-specific problem) 🔸 Resolved instance type restriction (Free Tier eligibility) 🔸 Handled Git large file error by cleaning .terraform and using .gitignore 👉 These are the exact issues you face in real production environments. 📌 Key Learnings: ✔ Modular Terraform = scalable infrastructure ✔ Proper Git practices are critical in DevOps ✔ Debugging skills matter more than just writing code 🔗 GitHub Project Link: https://lnkd.in/d4JKWgGE #DevOps #Terraform #AWS #InfrastructureAsCode #CloudEngineering #SRE #GitHub #LearningInPublic

Built and automated AWS infrastructure using Terraform — from event-driven monitoring to a serverless API ☁️🚀 Over the past few weeks, I’ve been working on two cloud projects focused on Infrastructure as Code and DevOps automation. 🔹 EC2 State Change Monitoring System 🔹 Serverless API (API Gateway + Lambda + DynamoDB) Instead of treating these as separate builds, I focused on end-to-end system design and automation. ⚙️ What was implemented Event-driven monitoring using EventBridge → Lambda → SNS API-based backend using API Gateway → Lambda → DynamoDB Infrastructure defined and deployed using Terraform CI/CD pipeline using GitHub Actions 🧠 Where Terraform fits Terraform acted as the control layer, handling: Infrastructure provisioning Service dependencies Lambda packaging (archive_file) Consistent deployments across environments 🔄 DevOps workflow Code → GitHub → CI/CD → Terraform → AWS Remote state stored in S3 State locking via DynamoDB Automated deployments from GitHub 🔍 Key troubleshooting lessons Fixed CI/CD failures caused by malformed AWS credentials Resolved Lambda runtime issues using CloudWatch logs Handled Terraform state conflicts by implementing remote backend Debugged resource duplication due to missing shared state Corrected handler and packaging issues in Lambda 🔥 Key takeaway Building infrastructure is one thing — making it automated, consistent, and reliable is what defines DevOps. #Terraform #AWS #DevOps #AWS #Terraform #DevOps #CloudComputing #InfrastructureAsCode #Serverless #GitHubActions #CloudEngineering #DevOpsEngineer #BuildInPublic

/🚀 Just Learned Helm – A Game Changer in Kubernetes! Today I explored Helm, and honestly, it makes working with Kubernetes 10x easier. 🔹 What is Helm? Helm is a package manager for Kubernetes that helps you deploy applications using pre-configured templates called charts. 💡 Before Helm: Managing multiple YAML files 😵 Deployments, services, configs… everything manual. ⚡ With Helm: One command = Full application deployment 🚀 Clean, scalable, and production-ready. 🔧 Some useful commands I learned: - "helm install my-app nginx" - "helm upgrade my-app nginx" - "helm uninstall my-app" - "helm list" - "helm rollback my-app 1" 🔥 Best part? Using Bitnami charts, I can deploy databases like PostgreSQL, Redis in seconds without complex setup. 📌 My Key Takeaways: - Kubernetes = Platform - Helm = Package Manager - Bitnami = Ready-to-use Apps Still exploring more into DevOps & Kubernetes, but this step really boosted my confidence 💪 Big thanks to my mentor Shubham Londhe for guiding me through this journey 🙌 #DevOps #Kubernetes #Hlm #CloudComputing #LearningInPublic #TechJourney Bitnami TrainWithShubham Google Amazon Web Services (AWS) Microsoft Azure

🚀 Mastering Terraform: 6 Essential Commands Every DevOps Engineer Must Know If you're working with Infrastructure as Code using Terraform, these commands are your daily toolkit. Let’s break them down in a simple and practical way 👇 --- 🔹 1. terraform init This is the first command you run in any Terraform project. It initializes your working directory by: ✔ Downloading required provider plugins (Azure, AWS, GCP, etc.) ✔ Setting up backend configuration (like remote state storage) ✔ Preparing your environment for execution 👉 Without "init", nothing works — it's the foundation. --- 🔹 2. terraform plan Think of this as a preview mode. It shows what Terraform will do before actually making changes: ✔ Resources to be created ✔ Resources to be modified ✔ Resources to be destroyed 👉 Helps avoid surprises in production. --- 🔹 3. terraform fmt This command formats your Terraform code: ✔ Aligns indentation ✔ Standardizes syntax ✔ Improves readability 👉 Clean code = better collaboration in teams. --- 🔹 4. terraform validate Used to check whether your configuration is syntactically valid: ✔ Detects errors in .tf files ✔ Ensures correct structure ✔ Doesn’t connect to cloud providers 👉 Fast way to catch mistakes early. --- 🔹 5. terraform apply This is where the real action happens 🚀 ✔ Creates infrastructure ✔ Updates existing resources ✔ Applies changes from your configuration 👉 Always review with "plan" before running "apply". --- 🔹 6. terraform destroy Used to tear down infrastructure: ✔ Deletes all resources defined in your config ✔ Useful for cleanup and cost control 👉 Use carefully — it’s irreversible ⚠️ --- 💡 Pro Tip: A typical workflow looks like this: "init → fmt → validate → plan → apply → destroy (when needed)" --- 🔥 Master these commands and you're already ahead in your DevOps journey! #Terraform #DevOps #Cloud #InfrastructureAsCode #Azure #AWS #GCP #Automation #SRE #Learning#DevOps Insighter

🚀 End-to-End DevOps CI/CD Pipeline Implementation on AWS I recently completed a hands-on project focused on building and automating the deployment of a containerized application using AWS and modern CI/CD practices. https://lnkd.in/edPryFaF 🔹 Project Overview Designed and implemented a complete CI/CD pipeline to automate application build, containerization, infrastructure provisioning, and deployment on AWS. 🔹 Key Responsibilities & Implementation • Containerized application using Docker for consistent and portable deployment • Configured GitHub Actions to automate: * Docker image build process * Secure push of images to AWS Elastic Container Registry (ECR) • Provisioned cloud infrastructure using Terraform (Infrastructure as Code): * Created custom VPC with networking components * Configured Security Groups for controlled access * Deployed EC2 instance for application hosting • Automated deployment workflow: * Pulled latest Docker image from ECR to EC2 * Deployed and managed container lifecycle on EC2 🔹 Technology Stack Docker | GitHub Actions | AWS (ECR, EC2, VPC) | Terraform | Linux 🔹 Key Outcomes ✔ Built a fully automated CI/CD pipeline from code commit to deployment ✔ Gained practical experience with Infrastructure as Code (IaC) ✔ Strengthened understanding of cloud-based container deployment #CloudEngineering#AWS #Terraform #Docker #GitHubActions #DevOps #CI_CD #InfrastructureAsCode #NewZealandJobs

🚀 Terraform Concept Every DevOps Engineer Must Know 👉 Implicit vs Explicit Dependency 🔍 What is Dependency in Terraform? Dependency defines the 𝗼𝗿𝗱𝗲𝗿 𝗼𝗳 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻. Terraform uses a 𝗗𝗔𝗚 (𝗗𝗶𝗿𝗲𝗰𝘁𝗲𝗱 𝗔𝗰𝘆𝗰𝗹𝗶𝗰 𝗚𝗿𝗮𝗽𝗵) to decide what runs first. ⚡ Implicit Dependency (Automatic) 👉 When one resource references another Example: EC2 uses Security Group → Terraform auto-manages order ✔ No extra code ✔ Clean & scalable ✔ Best practice 💡 Insight: "Terraform is smart enough to understand relationships if you write clean references." ⚙️ Explicit Dependency (Manual) 👉 Defined using depends_on Used when Terraform 𝙘𝙖𝙣𝙣𝙤𝙩 𝙙𝙚𝙩𝙚𝙘𝙩 𝙙𝙚𝙥𝙚𝙣𝙙𝙚𝙣𝙘𝙮 ✔ Full control on execution ✔ Useful for hidden dependencies ⚠️ But: Overuse makes code complex 🎯 Real Interview Tip 💬 Best Answer: "I prefer implicit dependencies for cleaner architecture and rely on explicit dependencies only when Terraform cannot infer relationships." 🔥 Pro Tip If you are using too many depends_on 👉 You are solving the problem incorrectly 📌 Final Thought "Good engineers write Terraform code. Great engineers design dependency flow." #Terraform #DevOps #Cloud #AWS #Azure #InfrastructureAsCode #Automation #DevOpsEngineer Learning with DevOps Insiders

🚀 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

 **𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 𝗘𝘃𝗲𝗿𝘆 𝗗𝗲𝘃𝗢𝗽𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗠𝘂𝘀𝘁 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱** Managing infrastructure manually vs using Terraform? The difference is HUGE. Let’s break it down with simple real-world scenarios 👇 💡 **𝗖𝗮𝘀𝗲 𝟭: 𝗖𝗿𝗲𝗮𝘁𝗲 𝘃𝗶𝗮 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺** Define a Resource Group in `.tf` → Run `terraform apply` ✅ Created in Azure + tracked in state file 👉 Perfect sync between code and infrastructure ❌ **𝗖𝗮𝘀𝗲 𝟮: 𝗥𝗲𝗺𝗼𝘃𝗲 𝗳𝗿𝗼𝗺 𝗖𝗼𝗱𝗲** Delete resource block → Run `terraform apply` 💥 Resource gets destroyed from Azure + removed from state 👉 Code controls lifecycle ⚠️ **𝗖𝗮𝘀𝗲 𝟯: 𝗗𝗲𝗹𝗲𝘁𝗲𝗱 𝗳𝗿𝗼𝗺 𝗣𝗼𝗿𝘁𝗮𝗹 (𝗠𝗮𝗻𝘂𝗮𝗹 𝗖𝗵𝗮𝗻𝗴𝗲)** Someone deletes it from Azure Portal 😬 👉 Terraform detects drift during `plan/apply` 👉 Recreates resource (since it still exists in code) 🔄 **𝗖𝗮𝘀𝗲 𝟰: 𝗥𝗲𝗻𝗮𝗺𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲** Change name in `.tf` 👉 Terraform destroys old resource 👉 Creates a new one with updated name ⚠️ (Because many resources don’t support rename in-place) 🧩 **𝗖𝗮𝘀𝗲 𝟱: 𝗜𝗺𝗽𝗼𝗿𝘁 𝗘𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲** Already created in portal? 👉 Use `terraform import` to bring it into state 👉 Then manage it via code 🔥 **𝗖𝗼𝗿𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:** 👉 Terraform only manages what’s in **Code + State** 👉 Manual changes = **Drift** 👉 No import = No control 💬 **𝗚𝗼𝗹𝗱𝗲𝗻 𝗟𝗶𝗻𝗲:** **“In Terraform, your code defines reality — everything else is drift.”** learning with DevOps Insiders #Terraform #DevOps #Azure #InfrastructureAsCode #Cloud #Automation #Learning #Tech