🚀 Learning Update | Docker & DevOps Fundamentals Here’s what I worked on recently: 🔹 Docker Concepts Studied core Docker concepts including: • Dockerfile • Image layers & caching • Best practices for efficient builds 🔹 Hands-on Implementation Created a multi-stage Dockerfile for a Node.js application to improve build efficiency. 🔹 Optimization Reduced image size using: • .dockerignore • Slim base images • Layer caching techniques ⚡ 🔹 Docker Compose Setup Built a setup with: • Node.js service • PostgreSQL service 🔹 Testing & Configuration • Verified services build, run, and communicate correctly • Configured environment variables, volume mounts, and health checks 🔹 Code Sharing Pushed Dockerfile and docker-compose.yml to GitHub for reference and reuse. Strengthening my DevOps fundamentals step by step. #Docker #DevOps #NodeJS #PostgreSQL #LearningInPublic #GrowthMindset

🗓️ Day 35/100 — 100 Days of AWS & DevOps Challenge Containerization chapter begins. Today: installing Docker CE and Docker Compose on the app server. Simple task on the surface — but worth explaining what's actually being installed, because it's not just one thing. Below are the commands to install Docker: $ sudo yum-config-manager --add-repo \  https://lnkd.in/gVPqThME $ sudo yum install -y docker-ce docker-ce-cli containerd.io docker-compose-plugin $ sudo systemctl start docker $ sudo systemctl enable docker $ sudo docker run hello-world #Testing for verification The Docker stack has three layers: docker-ce is the daemon — the background process that manages everything. docker-ce-cli is the command-line client that talks to it. containerd.io is the actual container runtime that creates and manages containers at the OS level. When you run docker run nginx, the CLI talks to the daemon, which talks to containerd, which uses runc to create the container. Three components working together. docker-compose-plugin vs the old docker-compose: The modern Compose is a Docker CLI plugin — invoked as docker compose (no hyphen). The old docker-compose with a hyphen was a separate Python binary and is now deprecated. If you see pipelines or docs using docker-compose, they're using legacy tooling. The modern version is faster, actively maintained, and ships as part of Docker's plugin architecture. Full Docker architecture breakdown + Q&A on GitHub 👇 https://lnkd.in/gKhHi-K6 #DevOps #Docker #Containers #Linux #100DaysOfDevOps #KodeKloud #LearningInPublic #CloudEngineering #Kubernetes #Containerization #CICD

Want your DevOps GitHub to actually stand out? Most profiles have tutorials. Recruiters want to see real systems. If you’re building a DevOps portfolio, projects like these make a real difference: 1. 3-tier web application Nginx + Python/FastAPI + PostgreSQL with Docker Compose 2. High-availability load balancer HAProxy + Keepalived with VIP failover on 2 nodes 3. Redis caching layer API + Redis with proper cache invalidation and TTL strategy 4. Blue-green deployment pipeline GitHub Actions deploying to two environments with rollback 5. Log centralization Loki + Promtail + Grafana with alerts for error spikes 6. Monitoring stack Prometheus + Alertmanager + node-exporter with real alert rules 7. Kubernetes application deployment Helm chart + health probes + HPA + resource limits 8. GitOps pipeline ArgoCD deploying from Git with auto-sync and drift detection 9. Terraform AWS infrastructure VPC + subnets + NAT + EC2 + ALB + autoscaling using clean modules 10. Secrets management Vault integration or Kubernetes sealed-secrets 11. Database backup automation PostgreSQL backups to S3 + tested restore script 12. CI security scanning Trivy + SBOM generation + fail build on critical vulnerabilities 13. Reverse proxy with TLS Nginx + Let’s Encrypt + auto renewal + security headers 14. Rate limiting & WAF simulation Nginx rate limiting + fail2ban + bot protection 15. Linux performance lab Debug CPU, memory, disk, and network using tools like top, iostat, ss, tcpdump Where beginners mess up: -Using full node:latest (huge images) npm install instead of npm ci (no lockfile) -Running as root (security audit fail) Copying entire codebase first (busts cache) Small tips: -Build these locally using VMs. •Build this locally: docker build -t myapp . && docker run -p 3000:3000 myapp •Watch your image shrink 80% vs basic Dockerfiles. This pattern scales to Kubernetes deployments perfectly. What's your go-to Dockerfile optimization? Still using node:latest? 😅 If you can run everything on your laptop like a mini datacenter, you’re already learning the right way. #DevOps #GitHub #CloudComputing #InfrastructureAsCode #TechLearning

Built and Deployed My First End-to-End DevOps Project I just completed a hands-on DevOps project where I built, containerized, and deployed a Flask application with a complete CI/CD pipeline. 🔧 Tech Stack: • Python (Flask) • Docker • Git & GitHub • GitHub Actions (CI/CD) • AWS EC2 💡 What I built: A Flask web app that dynamically displays the current time for: 🇺🇸 USA 🇨🇳 China 🇮🇳 India ⚙️ What makes this project special: Instead of just running locally, I implemented a full deployment pipeline: ✔️ Code pushed to GitHub ✔️ GitHub Actions triggers automatically ✔️ Secure SSH connection to EC2 ✔️ Docker container rebuilds and redeploys ✔️ Application updates live without manual intervention 🚧 Challenges I faced: • Docker container conflicts (port & naming issues) • GitHub authentication & SSH setup • CI/CD pipeline failures and debugging logs • YAML configuration errors 💥 Key Learnings: • Real DevOps is about debugging, not just building • CI/CD pipelines are the backbone of modern deployment • Docker + Automation = powerful combination • Small mistakes in YAML or ports can break entire systems 📈 What’s next: Planning to level this up with: • Nginx reverse proxy • Custom domain + HTTPS • Kubernetes deployment #DevOps #Docker #AWS #GitHubActions #Flask #CI_CD #CloudComputing #LearningInPublic

IaC - Terraform vs Pulumi vs Crossplane They all provision infrastructure, but with different approaches. If you're managing infrastructure today, understanding these three tools is essential. Also, the workflows, abstractions, and control mechanisms vary significantly. Let's break down how each one differs 👇 Terraform → Declarative IaC using HCL (HashiCorp Configuration Language) → Plan → Apply → Manage state workflow → Mature ecosystem with extensive provider support → State management (local or remote) is critical Pulumi → Infrastructure as actual code ~ use TypeScript, Python, Go, C# → Brings software engineering practices to infrastructure → Type safety and compile-time checks built in → Manages stacks with encryption for secrets Crossplane → Kubernetes-native control plane for infrastructure → Treat infrastructure as Custom Resources (CRs) → GitOps-first approach with continuous reconciliation → Multi-cluster and multi-cloud orchestration at the platform level From declarative configs → to real programming languages → to Kubernetes-native control planes IaC is evolving ~ from scripts to code to control plane abstractions. Now, which tool should you pick? It depends on your context. - Most teams start with Terraform for its maturity and ecosystem. - But if you're already deep in Kubernetes, Crossplane makes sense. - If you prefer real code over config, Pulumi shine. Master one first ~ the concepts transfer. _________________________________________ 𝐄𝐧𝐫𝐨𝐥 𝐧𝐨𝐰! 𝐃𝐞𝐯𝐎𝐩𝐬 𝐂𝐨𝐡𝐨𝐫𝐭 𝟒 𝐢𝐬 𝐧𝐨𝐰 𝐨𝐩𝐞𝐧. If you're serious about becoming a world-class DevOps engineer in 2026, this is your path. This isn't another bootcamp. This isn't a tutorial hell with a certificate at the end. This is systems-based training for engineers ready to go from good to exceptional. WHAT YOU'LL BUILD Not toy projects. Not "hello world" apps. Real production-grade systems: → Multi-environment CI/CD pipelines with DevSecOps → Infrastructure as Code that scales across 3+ environments → Production observability with Prometheus, Grafana, and OpenTelemetry Join today 👉 https://lnkd.in/eS3t5NwE

From code commit to live deployment — fully automated. Here's how I built it. 🧵 I recently deployed a complete CI/CD pipeline for a Java Spring Boot application using a modern DevOps toolchain, and I want to break down exactly how it works. ⚙️ The Stack: → Jenkins for Continuous Integration → SonarQube for Code Quality Analysis → Trivy for Container Security Scanning → Docker for containerization → Amazon ECR as the container registry → Helm for Kubernetes packaging → ArgoCD for GitOps-based Continuous Delivery → Kubernetes for container orchestration → GitHub as the GitOps source of truth 🔄 The Flow: 1️⃣ Developer pushes code → Jenkins pipeline triggers automatically 2️⃣ SonarQube scans the code for bugs, vulnerabilities & code smells — bad code doesn't move forward 3️⃣ Jenkins builds the app & creates a Docker image 4️⃣ Trivy scans the Docker image for OS & dependency vulnerabilities before it goes anywhere 5️⃣ Clean image gets pushed to Amazon ECR 6️⃣ Helm chart is updated with the new image tag and pushed to GitHub 7️⃣ ArgoCD detects the change in the Git repo and syncs the Kubernetes cluster 8️⃣ Application is live — no manual steps, no SSH, no kubectl apply The beauty of this pipeline: security is baked in, not bolted on. SonarQube catches bad code early. Trivy catches vulnerable images before they ever reach production. Only clean, scanned, verified images make it to Kubernetes. This hands-on project helped me deeply understand: ✔ Shift-left security with SonarQube & Trivy ✔ GitOps principles in practice ✔ Helm chart structuring for real deployments ✔ ArgoCD application management & sync strategies ✔ End-to-end secure pipeline design 🎥 Full working demo in the video — check it out! Would love to hear how others are securing their CI/CD pipelines. Drop your thoughts below! 👇 #DevOps #Kubernetes #GitOps #ArgoCD #Jenkins #Helm #CICD #SonarQube #Trivy #AmazonECR #AWS #DevSecOps #K8s #CloudNative #SoftwareEngineering

🚀 Real DevOps is not about deployment — it’s about debugging. I built a Netflix Clone using: ✔ Jenkins CI/CD ✔ SonarQube + Trivy + OWASP ✔ Docker ✔ Amazon EKS ✔ ArgoCD (GitOps) ✔ Prometheus + Grafana 🔗 GitHub: https://lnkd.in/gfaDhEcZ But the real learning came from failures 👇 💥 Issues I faced: Docker permission errors in Jenkins SonarQube project conflicts Pipeline syntax failures YAML breaking Prometheus kubectl / Helm not installed PowerShell JSON issues ArgoCD OutOfSync / Missing Git pushing to the wrong repo ⚠️ Most critical issue: 👉 Kubernetes pods stuck with: "Too many pods" Root cause: Node capacity limit Fix: Scaled EKS node group This is something tutorials don’t teach. 💡 What I learned: 80% of DevOps problems are: Permissions Networking Resource limits Not code. 🔥 Final result: Git → ArgoCD → Kubernetes ✔ Automated deployment ✔ Self-healing infrastructure If you’re learning DevOps: Stop just following tutorials. Start breaking things and fixing them. #DevOps #AWS #Kubernetes #EKS #ArgoCD #Jenkins #GitOps #Cloud

🗓️ Day 28/100 — 100 Days of AWS & DevOps Challenge Today's task: a developer has in-progress work on a feature branch but one specific commit is ready and needs to go to master right now, without dragging the rest of the unfinished work along. This is exactly what git cherry-pick is for. # Find the commit hash on the feature branch $ git log feature --oneline # abc5678 Update info.txt ← this one # Switch to master and cherry-pick it $ git checkout master $ git cherry-pick abc5678 # Push $ git push origin master One commit. Surgically applied. Feature branch untouched. 1. Why not just merge the feature branch? - The feature branch has in-progress commits code that isn't tested, isn't ready, and would break things on master. git merge feature brings ALL of it over. Cherry-pick takes only what's ready. 2. When this pattern matters in production: - A critical bug fix lands on a development branch. You can't merge the whole branch, there are half-finished features alongside the fix. You cherry-pick the fix onto master and onto any active release branches. This is how security patches get backported across multiple versions in open source projects. Same concept, same tool. The command to find a commit by message when you don't have the hash handy: $ git log --all --oneline --grep="Update info.txt" Saves time when the branch has many commits and you're looking for one specific one. Full breakdown on GitHub 👇 https://lnkd.in/gVHV9qPc #DevOps #Git #VersionControl #CherryPick #GitOps #100DaysOfDevOps #KodeKloud #LearningInPublic #CloudEngineering #CICD #Hotfix

Built and published my SimpleTimeService – End-to-End DevOps Challenge 🚀 This project is more than a simple web app — it’s a complete DevOps workflow built to simulate production-style delivery. What’s included: 🔹 Minimal Python web application (FastAPI) 🔹 Secure Docker containerization (non-root, read-only filesystem) 🔹 Kubernetes deployment with probes, limits, and service exposure 🔹 Infrastructure provisioning with Terraform (AWS VPC + EKS) 🔹 CI/CD automation using GitHub Actions 🔹 Security scanning using Trivy 🔹 Automated Kubernetes manifest updates with immutable image tags Tech stack used: 🐳 Docker ☸️ Kubernetes (EKS) 🏗 Terraform ⚙️ GitHub Actions ☁️ AWS 🐍 FastAPI Pipeline flow: Code Push → Lint → Build → Test → Security Scan → Push Image → Update Manifest → Deploy Production practices implemented: ✅ Non-root container execution ✅ Read-only filesystem ✅ Resource requests & limits ✅ Liveness & readiness probes ✅ Vulnerability scanning ✅ Immutable image versioning ✅ Infrastructure as Code GitHub repo live now 💻 https://lnkd.in/gANtZR8V #DevOps #AWS #Kubernetes #Terraform #Docker #GitHubActions #EKS #CloudNative #PlatformEngineering #DevSecOps

🚀 𝗝𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱: 𝗧𝗵𝗲 𝘂𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 𝗰𝗵𝗲𝗮𝘁 𝘀𝗵𝗲𝗲𝘁 𝘁𝗵𝗮𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗯𝗼𝗼𝗸𝗺𝗮𝗿𝗸𝗲𝗱 𝗯𝘆 𝗲𝘃𝗲𝗿𝘆 𝗗𝗲𝘃𝗢𝗽𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗮𝗻𝗱 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿! If you're still struggling with CI/CD pipelines or copying random YAML snippets from Stack Overflow, this comprehensive guide by Anisul Islam is your salvation. 📚 𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: ✅ 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗱𝗲𝗲𝗽-𝗱𝗶𝘃𝗲 - Understand Workflows, Events, Jobs, Runners & Actions ✅ 𝟭𝟭 𝗯𝗮𝘁𝘁𝗹𝗲-𝘁𝗲𝘀𝘁𝗲𝗱 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 including:  • PR validation & path filtering for monorepos  • Multi-environment deployment pipelines with approval gates  • Matrix testing across OS platforms  • Container builds with layer caching  • OIDC authentication (credential-less cloud deployment!)  • Dynamic job generation for changed files ✅ 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗵𝗮𝗿𝗱𝗲𝗻𝗶𝗻𝗴 - Least privilege permissions, SHA pinning, secret management ✅ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 - Caching strategies, shallow clones, concurrency controls ✅ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 - Blue/green deployments, reusable workflows, composite actions Whether you're just starting with CI/CD or optimizing enterprise pipelines, this guide covers everything from 𝘯𝘱𝘮 𝘵𝘦𝘴𝘵 to Kubernetes deployments with GitHub Actions. 🔥 𝗠𝘆 𝗳𝗮𝘃𝗼𝗿𝗶𝘁𝗲 𝗽𝗮𝗿𝘁: The "When to Use What" matrix - finally know whether to use matrix builds vs reusable workflows vs composite actions! 📖 𝗖𝗵𝗲𝗰𝗸 𝗶𝘁 𝗼𝘂𝘁: https://lnkd.in/gMEVSBkA 💡 Pro tip: The OIDC authentication pattern alone will save you from rotating leaked AWS credentials at 2 AM. You're welcome. What GitHub Actions pattern do you use most? Drop a comment below! 👇 #GitHubActions #CICD #DevOps #Automation #CloudNative #DevOpsCommunity #GitHub #YAML #SoftwareEngineering #TechResources