Mastering Docker Volumes & Networking for Real-World DevOps

⚙️ Built a Production-Ready AWS EKS Infrastructure using Terraform + Kubernetes I’m excited to share a real-world DevOps project where I designed and deployed a complete 3-tier application (BMI Health App) on AWS EKS, fully automated using Terraform and Helm. 🔧 Tech Stack AWS | Terraform | Kubernetes (EKS) | Helm | Docker | Prometheus | Grafana | Loki 🏗️ Architecture Overview User → 🌐 ALB (Ingress) → 🎨 Frontend → ⚙️ Backend → 🗄️ PostgreSQL ↓ 📊 Monitoring & Logging Stack ✨ Key Features Implemented ✅ Fully automated infrastructure (VPC, Subnets, NAT, IGW) ✅ EKS cluster provisioning with Terraform ✅ Helm-based application deployment ✅ AWS Load Balancer Controller (ALB Ingress) ✅ Cluster Autoscaler (dynamic scaling) ✅ Monitoring: ➡️ Prometheus ➡️ Grafana ✅ Logging: ➡️ Loki + Promtail ✅ RBAC (Role-based access control) ✅ Metrics Server for real-time resource usage 🌐 Live Deployment Application exposed via AWS ALB: ✔ Accessible from browser ✔ Fully working frontend + backend + DB 📂 GitHub Repository 👉 https://lnkd.in/g_wRyePj 💡 What I Learned ➡️ Terraform state & backend management (S3) ➡️ Kubernetes networking & ingress (ALB) ➡️ Helm lifecycle & debugging ➡️ Real AWS dependency handling (ENI, NAT, ALB issues) ➡️ Building production-style infra, not just demos 🔥 This project reflects a real-world DevOps environment — from infrastructure provisioning to application deployment and monitoring. #AWS #Terraform #Kubernetes #EKS #DevOps #Cloud #Helm #Docker #Prometheus #Grafana #Loki #InfrastructureAsCode #SRE

End-to-End DevSecOps Pipeline with Kubernetes, Terraform & AWS. I worked on a project where I built a fully automated DevSecOps pipeline — from infrastructure provisioning to deployment and monitoring. The main goal was simple: Automate everything and integrate security at every stage. 🔧 What’s included? 1) Infrastructure as Code (Terraform) - Kubernetes cluster (kubeadm) created on EC2 - Remote state (S3) + IAM + Security Groups - Checkov used for IaC security scanning - Manual approval for apply (production safety) 2) Kubernetes (kubeadm setup) - Fully self-managed cluster (not EKS) - Worker nodes auto-join using SSM-based join command - App exposed via NodePort 3) CI Pipeline (Pull Requests) - Maven build & tests - OWASP Dependency Check (SCA) - SonarQube (SAST) - Trivy FS scan 👉 Focus: Shift-left security (non-blocking, visibility first) 4) CD Pipeline (on merge to main) - Docker build - Trivy image scan (blocking for critical vulnerabilities) - Push to Amazon ECR - Deploy to Kubernetes using kubectl - Automatic rollout verification 5) Monitoring - Prometheus + Grafana + Alertmanager - Real-time metrics and dashboards 🔐 What makes this interesting? End-to-end automation (even cluster creation) Security integrated at multiple layers Clear separation of CI (validation) vs CD (deployment) Real-world DevSecOps practices 🤖 Bonus I also used Claude AI to generate structured documentation for this project, along with custom hooks and a CLAUDE.md file to enforce constraints — all version-controlled in the repo. 📄 Full details (with architecture, screenshots, flows): 👉 Please check the document attached in this post 💻 GitHub Repository: 👉 https://lnkd.in/g7R6ddsC #DevOps #DevSecOps #Kubernetes #Terraform #AWS #Docker #CI_CD #Cloud #SRE #Monitoring #GitHubActions #Prometheus #Grafana

Built a production-safe deployment system with health-gated releases — Day 25 → Day 30 Extended the deployment architecture by refining the blue-green strategy with automated validation and safe traffic switching. Instead of manually controlling deployments, the system now follows a structured flow: * v1 (stable live version) * v3 (new deployment candidate) New versions are deployed in parallel, validated through health checks, and only then promoted via NGINX traffic switching. Key improvements: * Zero-downtime deployments using controlled traffic routing * Health check–based release gating (bad builds never go live) * Automatic rollback by simply not switching traffic on failure * CI/CD-driven deployments (GitHub Actions → EC2 → Docker) * Fixed networking between services (frontend, backend, database) * Production-style separation of deploy vs release Current architecture: User → NGINX → React → FastAPI → PostgreSQL → Docker → EC2 → CI/CD This phase completes a reliable deployment pipeline where: * Deployment happens automatically * Validation happens before exposure * Users are never impacted by broken releases Next focus: Kubernetes + Terraform for scalable infrastructure and orchestration Project: https://lnkd.in/gj4YWDis #DevOps #ZeroDowntime #CI_CD #Docker #NGINX #AWS #CloudEngineering

🚀 Delivered a full CI/CD pipeline on AWS — production-style deployment in action Recently worked on implementing an end-to-end CI/CD solution to automate application delivery from code commit to a live Kubernetes environment. The goal was to eliminate manual deployments and create a reliable, repeatable pipeline for shipping updates quickly and safely. Here’s what I built: 🔹 Containerized a Node.js application using Docker 🔹 Designed a Jenkins pipeline to automate build and deployment workflows 🔹 Integrated Amazon ECR for secure image storage 🔹 Provisioned infrastructure using Terraform (EKS cluster, networking, autoscaling) 🔹 Deployed applications to Kubernetes using Helm charts 🔹 Configured ALB Ingress to expose the service publicly Now, every code push automatically triggers: ➡️ Build → Push → Deploy → Rolling update on Kubernetes 🔧 What made this impactful wasn’t just the setup — it was the troubleshooting: • Resolved CI failures caused by merge conflicts in code and Dockerfiles • Fixed IAM permission issues affecting ECR access and EKS communication • Debugged Jenkins authentication and GitHub integration • Identified and corrected Helm chart/template errors • Restored Kubernetes access by fixing kubeconfig and RBAC issues Each issue required understanding how different layers interact — from application code all the way to cloud infrastructure. 💡 Key takeaway: Building pipelines is one thing — but being able to debug across the entire system is what makes the difference in real-world environments. ✅ Result: A fully automated CI/CD pipeline on AWS, deploying to EKS with zero manual intervention and exposing a live application through an Application Load Balancer. Grateful for the hands-on experience and the lessons learned along the way. Looking forward to applying this in real production environments. #DevOps #AWS #Kubernetes #Jenkins #Docker #Terraform #CloudEngineering #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

Just published a deep dive on how I designed and implemented a dual-pipeline CI/CD architecture for a full-stack Node.js application (EpicBook), separating: 🔹 Infrastructure Pipeline (Terraform) – provisions Azure resources (VM, VNet, MySQL) 🔹 Application Pipeline (Ansible) – configures the server and deploys the app using Nginx + PM2 Why separate pipelines? Clean boundaries = independent scaling, faster debugging, and reliable rollbacks. Key learnings:  • State management is critical in CI/CD systems  • Separation of infra and app pipelines improves scalability and debugging  • Real DevOps work is iterative and problem-solving driven The result:  Fully automated infrastructure provisioning (Terraform) Fully automated application deployment (Ansible + PM2) Continuous integration and delivery via Azure DevOps The application was successfully deployed and is accessible via browser 🔗 I’ve written a detailed breakdown (with pipelines, code,and architecture) here: https://lnkd.in/ey2Gzykw #DevOps #Azure #Terraform #Ansible #CICD #InfrastructureAsCode

From Monitoring to Mastery: DockPulse v1.1 is Live 🚀🐳 Today isn’t just a version bump—it’s a shift in how you manage Docker. DockPulse has evolved from a passive monitoring tool into an action-driven control layer for your container ecosystem. If you've ever jumped between terminals, logs, and dashboards trying to debug one issue—you’ll understand exactly why this matters. We wanted one place. One workflow. Zero friction. So we built it. ━━━━━━━━━━━━━━━━━━━━━━ 🌟 What’s New in v1.1? ━━━━━━━━━━━━━━━━━━━━━━ 🔍 Smart Log Search & Highlighting Stop scrolling endlessly. Filter live logs in real-time, instantly highlight matches, and hide the noise—without interrupting the stream. ⚡ One-Click Web Terminal Jump inside any container directly from your browser. No SSH. No context switching. Just execute and move on. 🔔 Unified Notification Hub Email, Slack, Discord, Webhooks—all in one place. Configure once, stay informed everywhere. Get alerts and RCA exactly where your team collaborates. 🧹 Zero-Ghost Management No more stale containers lingering in your UI. DockPulse auto-syncs with Docker to keep your dashboard clean and accurate. 💎 Refined Premium Experience A redesigned interface with a glassmorphic dark theme, cleaner navigation, and sharper system insights. Built for clarity, not clutter. ━━━━━━━━━━━━━━━━━━━━━━ 🚀 Get Started in Seconds ━━━━━━━━━━━━━━━━━━━━━━ 📦 docker pull ajitrai9878/dockpulse:latest 🔗 https://lnkd.in/gzfQ3bn2 DockPulse is still lightweight. Still open-source. Still built for real-world DevOps—not over-engineered dashboards. Appreciate everyone following along this build-in-public journey. This is just the beginning. #Docker #DevOps #OpenSource #ProductLaunch #SoftwareEngineering #NodeJS #Cloud #Automation #BuildInPublic #Containers

🚀 From Code to Production — A Real-World DevOps Story Ever wondered what actually happens after a developer pushes code? Here’s a simple story from my daily work 👇 👨💻 A developer pushes code to GitHub ⬇️ ⚙️ GitHub Actions kicks off automatically Maven builds the application Tests run (quality checks ✅) Docker image gets created ⬇️ 📦 The image is pushed to AWS ECR (our secure registry) ⬇️ ☸️ Deployment begins in EKS (Kubernetes) Kubernetes detects new image version Scheduler decides where to run pods EC2 worker nodes pull the image from ECR Kubelet starts containers ⬇️ 🔄 Rolling update happens New pods come up Old pods are gradually removed Zero downtime 🚀 ⬇️ 🌐 Traffic is shifted to new version seamlessly 💡 The beauty of this flow? No manual intervention Fully automated Scalable & resilient Production-ready deployments in minutes This is what modern backend + DevOps looks like — not just writing code, but owning the full lifecycle. #DevOps #Java #SpringBoot #Kubernetes #AWS #EKS #Docker #GitHubActions #Microservices

🚨 “It worked on one server… but failed on another. Why?” This is exactly the kind of real-world DevOps problem I solved today while working with Ansible on AWS EC2 👇 💻 Task: ✔️ Setup Ansible cluster (1 Master + 2 Slaves) ✔️ Install Java on Slave1 ✔️ Install MySQL on Slave2 ✔️ Run a custom script on ALL nodes 😵 The Problem I Faced: After running my playbook, everything looked fine… but when I SSH’d into the server: ❌ Script not found ❌ File not created ❌ No errors in output 🔍 What was going wrong? 👉 My playbook was NOT actually running on the target host This can happen due to: Wrong inventory group ❌ Host mismatch ❌ Playbook targeting wrong hosts ❌ 💡 How I Debugged It (Step-by-Step): ✅ Verified inventory (/etc/ansible/hosts) ✅ Tested connectivity: ansible all -m ping ✅ Checked target hosts before execution: ansible-playbook run_script.yaml --list-hosts 🔥 This command is a GAME CHANGER → It tells you exactly where your playbook will run ⚙️ Final Working Playbook: - name: Run custom script on all hosts hosts: all become: yes tasks: - name: Create script copy: dest: /tmp/add_text.sh content: | #!/bin/bash echo "This text has been added by custom script" >> /tmp/1.txt mode: '0755' - name: Execute script shell: /tmp/add_text.sh 🎯 Key Learning: 👉 If something is not working in Ansible… It’s usually NOT the code — it’s the inventory or targeting 🚀 Pro Tip (Interview Ready): “I always validate host targeting using --list-hosts before running playbooks to avoid silent failures.” 💬 Let’s Discuss: Have you ever faced a situation where your automation ran successfully but did nothing? Drop your experience in comments 👇 🔁 Share this with someone learning DevOps 📌 Follow me for more real-world DevOps learnings #DevOps #AWS #Ansible #Automation #CloudComputing #LearningInPublic #TechCareers #InfrastructureAsCode #Debugging

𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 𝐒𝐭𝐚𝐭𝐞 𝐅𝐢𝐥𝐞: 𝐖𝐡𝐚𝐭 𝐢𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐢𝐬 𝐚𝐧𝐝 𝐰𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 Most people learning Terraform understand the basics quickly, you write config, you run apply, infrastructure gets created. But the state file? That's where things get interesting. Here's what you actually need to know: 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐬𝐭𝐚𝐭𝐞 𝐟𝐢𝐥𝐞 𝐝𝐨𝐞𝐬 Terraform keeps a record of every resource it manages. When you run a plan or apply, it compares that record against your config and the real world infrastructure, and figures out exactly what needs to change. Without it, Terraform has no memory. It wouldn't know what it built or what to touch. 𝐋𝐨𝐜𝐚𝐥 𝐯𝐬 𝐑𝐞𝐦𝐨𝐭𝐞 𝐬𝐭𝐚𝐭𝐞 By default the state file sits on your machine — fine for solo projects, risky for anything else. In a team environment you store it remotely. AWS S3 is the most common option. HCP Terraform (HashiCorp's own platform) is increasingly the recommended choice — it handles versioning, encryption, and locking out of the box. 𝐒𝐭𝐚𝐭𝐞 𝐥𝐨𝐜𝐤𝐢𝐧𝐠 When two people run Terraform at the same time against the same state, things can break badly. State locking prevents this — only one operation can hold the state at a time. DynamoDB handles this when using S3 as your backend. 𝐃𝐫𝐢𝐟𝐭 If someone goes into the console and manually changes something Terraform manages, your state file no longer reflects reality. That gap is called drift — and it's one of the more frustrating things to debug if you don't know what you're looking for. 𝐓𝐡𝐞 𝐠𝐨𝐥𝐝𝐞𝐧 𝐫𝐮𝐥𝐞 Never edit the state file directly. Terraform provides CLI commands for any state manipulation you need. Direct edits can cause Terraform to destroy and recreate resources unexpectedly. The state file isn't the most exciting part of Infrastructure as Code. But misunderstand it, and it will cost you. credit for image CoderCo #Terraform #InfrastructureAsCode #DevOps #CloudEngineering #CloudComputing #Automation