GitOps with ArgoCD vs CI/CD Pipelines

🚀 Week 06 – Jenkins CI/CD | #90DaysOfDevOps This week was all about understanding and implementing CI/CD pipelines using Jenkins — moving one step closer to real-world DevOps practices. 🔧 What I Built: Created an end-to-end CI/CD pipeline for a Django application Automated stages: ✅ Code Checkout (GitHub) 🐳 Docker Image Build 🧪 Basic Testing Stage 🚀 Deployment using Docker container Deployed the application on port 8000 via Jenkins pipeline ⚙️ Key Concepts Learned: Declarative Jenkins Pipelines Pipeline stages & execution flow Jenkins + Docker integration Handling build failures & debugging logs Importance of idempotent deployments 🐞 Challenges Faced (Real Learning 👇): ❌ Docker build failed due to missing build context (.) ➝ Fixed by understanding how Docker CLI works internally ⚠️ Container already running / port conflict issues ➝ Resolved by adding cleanup step before deployment ⚠️ Jenkins agent low disk space → going offline ➝ Learned how to monitor and clean workspace/logs ⚠️ Initial confusion with Docker permissions in Jenkins ➝ Understood user groups & Docker socket access 💡 Key Takeaway: CI/CD is not just about writing pipelines — it’s about debugging, reliability, and making deployments repeatable. 📂 GitHub Repo (Full Implementation + Code): 👉 https://lnkd.in/gvjyFEfe #DevOps #Jenkins #CICD #Docker #Automation #90DaysOfDevOps #Cloud #AWS #LearningInPublic

🚀 End-to-End CI/CD Pipeline with Jenkins + Docker I recently built a complete CI/CD pipeline to automate the process from code commit to deployment. Sharing my learning along with the pipeline code 👇 🔹 What This Pipeline Does Pulls code from GitHub Builds Docker image Pushes image to Docker Hub Deploys container automatically Provides success/failure feedback 🔹 Jenkins Pipeline Code 👇 pipeline { agent any environment { IMAGE_NAME = "your-dockerhub-username/my-app" TAG = "${BUILD_NUMBER}" } stages { stage('Checkout Code') { steps { checkout scm } } stage('Build Docker Image') { steps { sh 'docker build -t $IMAGE_NAME:$TAG .' } } stage('Login to Docker Hub') { steps { withCredentials([usernamePassword( credentialsId: 'docker-creds', usernameVariable: 'DOCKER_USERNAME', passwordVariable: 'DOCKER_PASSWORD' )]) { sh 'echo $DOCKER_PASSWORD | docker login -u $DOCKER_USERNAME --password-stdin' } } } stage('Push Image') { steps { sh 'docker push $IMAGE_NAME:$TAG' } } stage('Deploy Container') { steps { sh ''' docker stop my-container || true docker rm my-container || true docker run -d -p 80:80 --name my-container $IMAGE_NAME:$TAG ''' } } } post { success { echo '✅ CI/CD Pipeline executed successfully!' } failure { echo '❌ Pipeline failed!' } } } 🔹 Pipeline Flow 🔄 👉 Code → Build → Test → Push → Deploy → Live 🚀 🔹 Key Learnings 💡 ✅ CI/CD automates the entire workflow ✅ Docker ensures consistency across environments ✅ Jenkins pipelines make deployments reliable ✅ Version tagging is important (avoid latest) 🔹 Pro Tip ⚡ Always use secure credentials (withCredentials) and avoid hardcoding sensitive data in pipelines. 🔹 Outcome 🚀 ✔️ Automated deployments ✔️ Faster delivery ✔️ Reduced manual effort ✔️ Production-ready workflow #DevOps #CICD #Jenkins #Docker #Automation #Cloud #LearningJourney

🚀 Just built and deployed an End-to-End CI/CD Pipeline from scratch! A few weeks ago, I had no idea how Jenkins, ArgoCD, and Kubernetes worked together. Today, I have a fully automated DevOps pipeline running! 💪 Here's what the pipeline does automatically: ✅ Developer pushes code to GitHub ✅ Jenkins triggers automatically via Webhook ✅ Maven builds and tests the application ✅ SonarQube performs Static Code Analysis ✅ Docker builds the image and pushes to DockerHub ✅ Jenkins updates the deployment manifest in Git ✅ ArgoCD detects the change and deploys to Kubernetes ✅ Application is live with zero manual effort! 🛠️ Tech Stack Used: → Jenkins (CI Server) → Maven (Build Tool) → SonarQube (Code Quality) → Docker + DockerHub (Containerization) → ArgoCD (GitOps CD) → Kubernetes / Minikube (Orchestration) → AWS EC2 (Hosting) 💡 Key Learnings: → Understood the difference between CI and CD → Learned the GitOps Pull model vs traditional Push model → ArgoCD enforces Git as single source of truth — any manual Kubernetes change gets automatically reverted! → Distroless Docker images for minimal and secure containers 🙏 Huge shoutout and credits to Abhishek Veeramalla for his amazing free DevOps content on YouTube. This project is completely based on his tutorial series. If you're learning DevOps, his channel is a goldmine! 🔗 My GitHub Repo: https://lnkd.in/dAGJVuyU 🔗 Abhishek's Channel: https://lnkd.in/dSrqh6fh 🔗 Original Repo: https://lnkd.in/dPmEwDda If you're planning to learn DevOps, just start. The best way to learn is by building! 🔥 #DevOps #CICD #Jenkins #ArgoCD #Kubernetes #Docker #GitOps #SonarQube #AWS #LearningInPublic #DevOpsEngineer #CloudComputing

I've used GitHub Actions, GitLab CI, and Azure DevOps. Here's why I keep coming back to ArgoCD for production deployments — and when I don't. The problem with push-based CI/CD: Your pipeline pushes to the cluster. If something drifts — a manual kubectl apply, a failed rollback, a config change — your pipeline doesn't know. Your cluster is now lying to you. ArgoCD flips this. The cluster pulls from Git. Git is truth. If your cluster doesn't match Git, ArgoCD self-heals. You get drift detection out of the box. What I love about it in practice: → Rollbacks are just Git reverts — no pipeline magic needed → Every deploy is visible, auditable, and reproducible → Works beautifully with Helm + Kustomize → The UI is genuinely useful for oncall When I DON'T use ArgoCD: → Stateful apps with complex DB migrations (timing matters) → Very small teams where GitOps overhead > benefit → Non-Kubernetes workloads (wrong tool for the job) The honest take: ArgoCD isn't magic. It requires discipline in how you structure your repos. But once it clicks, going back to push-based deployments feels like deploying by FTP. Have you made the GitOps shift? What broke when you did? #ArgoCD #GitOps #Kubernetes #CICD #DevOps ☢️

🚀 Getting Started with GitLab CI/CD Pipeline I’ve been learning and implementing GitLab CI/CD pipelines to automate build, test, and deployment workflows. This implementation is inspired and guided by learnings from TrainWithShubham. 🔹 GitLab Overview GitLab supports end-to-end DevOps lifecycle Can be used as cloud (GitLab.com) or self-hosted Built-in CI/CD pipelines (no external tool needed) 🔹 Project Setup Created a new project and repository Configured SSH keys for secure access Structured project using groups and naming conventions (as shown in page 1 notes) 🔹 CI/CD Pipeline (.gitlab-ci.yml) Defined pipeline stages: 👉 build → test → push → deploy (page 2 notes) Created jobs for each stage: build_job test_job push_job deploy_job 🔹 Key Features Implemented Used GitLab Runners (Project & Instance runners) (page 2) Defined custom variables & secrets (CI/CD variables) Implemented artifacts to store logs (page 3) Added tags to control runner execution Automated logging and validation inside jobs 🔹 Pipeline Workflow Build → creates application/docker build Test → validates and stores logs Push → pushes to Docker Hub Deploy → deploys to EC2 instance 📊 Successfully executed full pipeline with all stages passing (as shown in pipeline result screenshot page 6) 💡 Key Learning: CI/CD is not just automation — it ensures consistency, reliability, and faster delivery. 🙏 Special thanks to Shubham Londhe for the guidance and structured learning. #GitLab #CICD #DevOps #Automation #Docker #AWS #CloudComputing #TrainWithShubham #FresherProjects

*From Manual Deployments to Full GitOps Control (ArgoCD on EKS)* I stopped deploying manually… and everything changed. No more kubectl apply. No more guessing what’s running in the cluster. I just completed Phase 2 of my self-directed DevOps journey—this time with GitOps on a live AWS EKS cluster. And here’s what surprised me… I pushed YAML to GitHub… and ArgoCD deployed everything automatically. No touch. No manual steps. Then I tested it. I changed replicas from 2 → 3 in one commit. Within 60 seconds, the cluster updated itself. So I tried breaking it. I manually scaled it down to 1. ArgoCD reverted it back to 3. That’s when it clicked: In GitOps, Git is the source of truth, not the cluster. I went further: → Upgraded nginx from 1.25 → 1.26 with zero downtime → Rolled back with a single commit → Used Kustomize to manage dev, staging, and prod from one base → Added a new environment with just a few lines using ApplicationSet → Synced secrets securely using ESO + AWS Secrets Manager (nothing exposed in Git) Every deployment? Tracked. Versioned. Traceable. 4 deployments. 4 commits. Full audit trail. No guesswork. No drift. Just clean, controlled infrastructure. This is what real-world DevOps feels like. Still learning. Still building. #DevOps #GitOps #Kubernetes #AWS #EKS #ArgoCD #CloudEngineering #InfrastructureAsCode #SRE #CloudComputing #TechJourney #LearningInPublic #BuildInPublic

CI/CD with GitHub Actions + Docker Over the past few days, I’ve been exploring how to build a complete CI/CD pipeline using GitHub Actions and Docker — and it’s a game changer 🔥 💡 What I learned: ✅ How workflows are defined using YAML ✅ Difference between push, pull_request, and workflow_dispatch ✅ Using uses: to integrate reusable actions ✅ Managing env variables & secrets securely ✅ Conditional execution using if ✅ Building & pushing Docker images automatically ⚙️ CI/CD Flow (Simplified): 1️⃣ Developer pushes code / raises PR 2️⃣ GitHub Actions triggers workflow 3️⃣ Runner sets up environment 4️⃣ Code is built & tested 5️⃣ Docker image is created & pushed 6️⃣ Deployment happens to target environment (Dev / Staging / Prod) 🔥 Key Takeaways: Automating workflows saves huge time ⏱️ CI ensures code quality before merge CD enables faster & reliable deployments Docker ensures consistency across environments 🎯 Pro Tip: Use environment-based deployments + secrets to manage dev, staging, and production securely. 📊 I’ve also created a visual diagram to better understand the workflow and pipeline 👇 #GitHubActions #DevOps #Docker #CICD #SoftwareEngineering #WebDevelopment #Cloud #Automation #LearningJourney

🗓️ Day 32/100 — 100 Days of AWS & DevOps Challenge Today's task: incorporate the latest master changes into a feature branch — no merge commit, no lost work, clean linear history. That's git rebase. $ git checkout feature $ git rebase master $ git push -f origin feature Three commands. But the concept is worth understanding properly because this is one of the Git topics that separates engineers who know commands from engineers who understand what's actually happening. What rebase does — visually: Before: master: A ── B ── C feature: A ── B ── D ── E After git rebase master (from feature): master: A ── B ── C feature: A ── B ── C ── D' ── E' The developer's commits (D, E) are fully preserved — every change is still there. Rebase doesn't delete them. It replays them with C as the new base instead of B. Same changes, new parent, new hashes. That's why it's D' and E' — logically identical, technically different commits. Compare that to git merge: After git merge master (from feature): feature: A ── B ── D ── E ── M (merge commit)         \  /         ──── C ─── Same data, but now there's an extra merge commit M that adds noise to the history. On a repo where dozens of features are being merged weekly, that adds up fast. The force push after rebase: git push -f origin feature is required because rebase rewrote the commit hashes. The remote still has the original D and E. After rebase, local has D' and E' — Git sees them as different histories and rejects a normal push. Force is needed. This is safe here because feature is the developer's own branch. Nobody else has pulled it. The golden rule: never force push a shared branch that others have based work on. Full rebase vs merge breakdown on GitHub 👇 https://lnkd.in/gKnai5Yc #DevOps #Git #GitRebase #VersionControl #GitOps #100DaysOfDevOps #KodeKloud #LearningInPublic #CloudEngineering #CICD #CleanHistory

🚀 Implemented an End-to-End CI/CD Pipeline with GitOps Today I worked on setting up a complete CI/CD pipeline integrating multiple DevOps tools across build, security, and deployment stages. 🔧 What I implemented: * Provisioned AWS infrastructure and configured access * Set up Jenkins for CI pipeline automation integrated with GitHub * Integrated SonarQube for code quality analysis * Built Docker images and performed vulnerability scanning using Trivy * Pushed images to Docker Hub * Provisioned a Kubernetes cluster using kops * Deployed application using Kubernetes manifests * Implemented GitOps-based deployment using ArgoCD (running inside Kubernetes)  * Synced application state directly from Git repository  * Managed deployments declaratively --- 🔄 Pipeline flow: GitHub → Jenkins (Build + SonarQube + Docker + Trivy + Push) → Docker Hub → ArgoCD → Kubernetes --- ⚠️ Key learnings: * Importance of consistent image naming across pipeline and deployment * Handling private registry authentication using imagePullSecrets * Debugging SonarQube processing and Jenkins configuration issues * Understanding CI (Jenkins) vs CD (ArgoCD) separation --- This implementation gave me deeper hands-on experience in building production-style CI/CD pipelines using GitOps principles. #DevOps #CI_CD #Kubernetes #Jenkins #Docker #ArgoCD #Cloud

Are we quietly trading one set of Kubernetes problems for another? GitOps with Git gave us something genuinely valuable: history, blame, diffs, and PR-based review as first-class delivery primitives. Now OCI is taking over as the transport layer for Kubernetes config, for good reasons: Content-addressability, immutability, and edge and airgap compatibility are real advantages that Git was never designed to provide. Instead of choosing between them, I built a way to have both: Kokumi. Kokumi is a Kubernetes config delivery tool built on the premise that you shouldn't have to choose. It models delivery as three distinct concerns: what should be built, what was built, and what is currently running. Rendering and deployment are fully decoupled, which means: 🔍 Inspect & edit: Full manifest review in a built-in UI before any cluster sees it. 📝 Diff & approve: Review a structured diff of every change before it's applied. ⏪ Instant rollback: Repoint to any previous artifact. Already built, already there. This is early, and I want to pressure-test the model with people who've actually felt this pain. If you're working through OCI delivery architecture: Where does the model break for your use case? What is missing? 👉 Repo link, would love your feedback: https://lnkd.in/d-p_mhjS #Kubernetes #GitOps #CloudNative #DevOps #PlatformEngineering