GitHub Actions Comprehensive Guide by Anisul Islam

🚀 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

🚀 Built a Full DevSecOps Pipeline on AWS (with Real-World Debugging) I recently deployed a Netflix Clone application using a complete DevSecOps setup: 🔧 Jenkins → CI/CD 🔐 SonarQube + Trivy + OWASP → Security 🐳 Docker → Containerization ☸️ Amazon EKS → Kubernetes 🔁 ArgoCD → GitOps 📊 Prometheus + Grafana → Monitoring 🔗 GitHub: https://lnkd.in/gfaDhEcZ 💥 This wasn’t a smooth tutorial project — I hit real production-like issues: Docker permission errors in Jenkins SonarQube project conflicts Kubernetes pods stuck in Pending → “Too many pods.” YAML & Helm configuration issues PowerShell breaking kubectl commands ArgoCD showing OutOfSync / Missing Service not accessible due to the wrong type 🔥 Biggest lesson: 👉 DevOps is NOT about tools 👉 It’s about debugging infrastructure ✅ outcome: Fully automated pipeline Git → ArgoCD → EKS deployment Self-healing Kubernetes setup Publicly accessible application 💡 Most critical issue I solved: “Too many pods” → Node capacity limitation → Fixed by scaling EKS nodes This is something most tutorials don’t cover. 🚀 Key takeaway: 80% of DevOps problems are: Networking Permissions Resource limits If you're learning DevOps: Stop just following tutorials. Start breaking things and fixing them. #DevOps #AWS #Kubernetes #EKS #ArgoCD #Jenkins #DevSecOps #Cloud #GitOps

Excited to announce rustyochestrator v0.1.4 :— the biggest release yet for our open-source Rust CI/CD pipeline runner. What's new: -- Task Timeouts & Configurable Retries :— set per-task or pipeline-wide timeouts ("5m", "1h") and retry strategies including exponential backoff. No more hanging builds. -- Task Output Capture & Reuse :— tasks can export values (outputs: [VERSION, BUILD_ID]) and downstream tasks consume them via ${{ tasks.build.outputs.VERSION }}. Real data flow between pipeline stages. -- Conditional Execution :— if: expressions evaluate env vars, task outcomes, and boolean logic. Skipped tasks don't block dependents. -- Matrix Builds :— full cartesian product expansion with include/exclude support. One workflow definition, multiple parallel configurations. -- Local Artifact Store :— upload-artifact and download-artifact from GitHub Actions are emulated locally. Debug with --keep-artifacts. -- Structured Run Reports :— every run produces a JSON report with per-task timing, cache hits, and bottleneck identification. View with rustyochestrator report --markdown. -- New CLI Flags :— --dry-run (preview without executing), --trace-deps (visualise dependency resolution), --verbose (stream all output), --log-file (write to file for CI). rustyochestrator parses your GitHub Actions workflows and runs them locally with parallel DAG scheduling, content-addressable caching, and a live TUI dashboard. No Docker required. Written in Rust. Installs in seconds. cargo install rustyochestrator GitHub: https://lnkd.in/etC5ZMh6 If you're tired of pushing to CI just to find out your workflow is broken, this is for you. Star the repo if it saves you time. #rust #rustlang #devops #cicd #opensource #github #automation

Beyond the Code: Architecting a Hybrid-Cloud DevSecOps Pipeline I’m thrilled to share that I have successfully deployed my latest project—a professional Python microservice—live on an AWS EC2 instance using a custom, hybrid CI/CD architecture! Most projects stop at "it works on my machine." I wanted to build something that reflects real-world enterprise standards. This project wasn't just about writing Python; it was about orchestrating a secure, automated path from the first line of code to a live production server. The Technical Core Application: A high-performance FastAPI microservice with a modern, responsive dashboard styled via Tailwind CSS. The CI Layer (GitHub): Automated unit testing and linting using GitHub Actions to ensure every Pull Request is production-ready. The "Enterprise" Layer (GitLab): I configured a Self-Hosted GitLab Runner on an AWS EC2 instance to handle deep security analysis and Docker builds. Security & Quality: Integrated SonarQube as a mandatory Quality Gate, ensuring zero vulnerabilities and high code coverage before deployment. The AWS Deployment The final stage of the pipeline uses automated SSH-based deployment to manage a containerized environment on AWS. By using Docker-in-Docker (DinD) and secure secret management, the application is seamlessly updated without manual intervention. Key Lessons Learned: Self-Hosted Infrastructure: Configuring my own GitLab Runner on EC2 provided deep insights into Linux administration, Docker executors, and cloud networking. DevSecOps Integration: Security isn't a final step; it’s a constant. SonarQube taught me how to catch technical debt before it becomes a problem. Hybrid Orchestration: Learning to bridge GitHub and GitLab showed me how to design flexible, tool-agnostic workflows. A huge thank you to the community for the guidance during this build! Check out the live code and the full architecture on GitHub:https://lnkd.in/eGYU99bq #DevOps #CloudEngineering #AWS #Python #FastAPI #GitLab #GitHubActions #SonarQube #Docker #SoftwareEngineering #TechNigeria #DevSecOps #CloudComputing2026 #PythonDevelopment #DevOpsProject

⚛️ Helm is great. Until it isn't. You start with 2 charts. Then 5. Then 15 microservices, 3 environments, 2 clusters, and a bash script held together with hope. That bash script IS your deployment system. And nobody wants to touch it. I went deep on Helmfile — the declarative orchestration layer that sits above Helm and gives you what Helm was never designed to provide: → One `helmfile apply` to sync your entire platform → `helmfile diff` — see exactly what changes BEFORE it hits prod → `needs:` — dependency ordering with a DAG, not guesswork → Environment-aware values without duplicating configs → SOPS + Vault native secret management → Kustomize, raw YAML, hooks — all as Helm releases The part that changed how I think about deployments: Helmfile uses a two-pass rendering engine. ⭐ Pass 1 resolves your environment values. ☀️ Pass 2 re-renders the entire state file with that context — which means your release names, value file paths, and chart versions can all be dynamically constructed per environment. Template your templates. And `helmfile show-dag` will print your entire execution graph — which releases run in parallel, which wait for dependencies — before you run anything. If you're managing Helm at scale, this is the missing control plane. Full technical breakdown in the blog https://lnkd.in/gXcn4BVU #Kubernetes #Helm #DevOps #GitOps #Platform Engineering #SRE #CloudNative

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

How to set up CI/CD for a .NET API using GitHub Actions Still deploying manually or relying on scripts? That approach breaks as your system grows. GitHub Actions gives you a simple, powerful way to automate build and deployment directly from your repo. Here’s a practical flow: 1. Push your code to GitHub Keep your main branch stable and production-ready. 2. Create a workflow file Create .github/workflows/dotnet.yml This defines your entire CI/CD pipeline. 3. Trigger on every push on: push: branches: [ "main" ] 4. Set up the runner Use a managed VM from GitHub: jobs: build: runs-on: ubuntu-latest 5. Install .NET - name: Setup .NET uses: actions/setup-dotnet@v4 with: dotnet-version: 8.0.x 6. Build and test - run: dotnet restore - run: dotnet build --configuration Release - run: dotnet test --no-build 7. Publish your app - run: dotnet publish -c Release -o ./publish 8. Deploy to Azure Use official actions like: azure/webapps-deploy azure/container-apps-deploy Authenticate using GitHub Secrets. 9. Manage secrets securely Store credentials in GitHub Secrets. Never commit them to code. Key Insight GitHub Actions turns your repository into your deployment engine. Every commit becomes: Code → Build → Test → Deploy No external tools required. If you're building .NET apps, this is one of the fastest ways to set up CI/CD with minimal friction. Are you using Azure DevOps pipelines or GitHub Actions for your workflows? #githubactions #cicd #dotnet #devops #azure #cloudcomputing #softwareengineering #automation #microservices #backenddevelopment

𝗨𝗽𝗱𝗮𝘁𝗲: 𝗧𝗼𝗼𝗸 𝗺𝘆 𝗖𝗹𝗼𝘂𝗱𝗙𝗿𝗼𝗻𝘁 + 𝗦𝟯 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗳𝗿𝗼𝗺 “𝘄𝗼𝗿𝗸𝗶𝗻𝗴” 𝘁𝗼 **𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲** 🚀 After my last post, I didn’t stop at just making it work. I pushed it further — automated deployments, secured access, and debugged some real-world issues that don’t show up in tutorials. Here’s what I upgraded and learned 👇 --- 🔧 **𝟭. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 (𝗖𝗜/𝗖𝗗)** Built a GitHub Actions pipeline to: * Sync files to S3 automatically on every push * Invalidate CloudFront cache No more manual uploads. Clean and repeatable deployments. --- 🔐 **𝟮. 𝗦𝗲𝗰𝘂𝗿𝗲𝗱 𝘁𝗵𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (𝗢𝗔𝗖)** Earlier, S3 was public. That’s not acceptable in real setups. 👉 Implemented: * Block Public Access * Origin Access Control (OAC) * Bucket policy allowing only CloudFront Now: ❌ Direct S3 access → Blocked (403) ✅ Only CloudFront can serve content --- 🐛 **𝟯. 𝗗𝗲𝗯𝘂𝗴𝗴𝗲𝗱 𝗥𝗲𝗮𝗹 𝗜𝘀𝘀𝘂𝗲𝘀 (𝗡𝗼𝘁 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀)** 🔴 504 Gateway Timeout Cause: Wrong origin endpoint + protocol mismatch Fix: Switched to S3 REST endpoint + proper origin config 🔴 CloudFront caching not working Cause: Forwarding all headers → cache miss every time Fix: Used optimized cache policy 🔴 403 Access Denied (after securing S3) Cause: Missing/incorrect OAC bucket policy + wrong default root object (`/index.html`) Fix: Correct policy + set `index.html` (without `/`) 👉 This one was subtle — `/index.html` vs `index.html` broke the entire setup. --- 💡 **𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀** * “Working” ≠ “Production-ready” * CloudFront behavior depends heavily on origin type (website vs REST) * Small config mistakes (like a leading `/`) can break everything * Security (OAC) is not optional --- 📦 **𝗙𝗶𝗻𝗮𝗹 𝗦𝗲𝘁𝘂𝗽** GitHub → CI/CD → S3 (private) → CloudFront (OAC + caching) --- This project went from basic setup to something that actually reflects real DevOps work — debugging, automation, and security. If you’re learning AWS/DevOps, don’t stop when it works. Break it, secure it, automate it. #AWS #CloudFront #S3 #DevOps #CICD #LearningByDoing Abhishek Veeramalla Shubham Londhe TrainWithShubham Vishakha Sadhwani

🚀 End-to-End Microservices Deployment on Kubernetes with ConfigMap, Secrets & Automation Excited to share that I’ve successfully deployed a production-style microservices application on Kubernetes (Minikube) with complete configuration management and automation 🚀 🔧 Tech Stack: Kubernetes (Minikube) Docker & Docker Compose Vagrant (VM setup) Java (Spring MVC + Tomcat) MySQL, Memcached, RabbitMQ, Elasticsearch 📦 What I Built: ✅ Microservices Deployment Deployed application, database, cache, messaging, and search services Used ClusterIP for secure internal communication ✅ Config Management (Production Approach) Implemented ConfigMap for non-sensitive configuration Used Secrets for DB credentials and sensitive data Injected environment variables into pods dynamically ✅ Persistent Storage Configured PersistentVolumeClaim (PVC) for MySQL Ensured data durability across pod restarts ✅ Automation (One-Click Setup) Created scripts to: Start Minikube + configure Docker environment Build Docker images Deploy complete Kubernetes stack Stop and clean environment Reduced manual setup effort significantly ⚡ 📊 Current Cluster Status: ✔️ All Pods Running ✔️ Services Healthy & Communicating ✔️ ConfigMap & Secrets Integrated ✔️ Application Fully Functional 🧠 Key Learnings: Real-world use of ConfigMap vs Secrets Kubernetes networking & service discovery Persistent storage (PVC) handling Debugging issues like CrashLoopBackOff & service connectivity Transition from Docker Compose → Kubernetes 🚀 Next Steps: ➡️ Ingress Controller (external access via browser) ➡️ CI/CD pipeline (GitHub Actions / Jenkins) ➡️ Deployment on AWS EKS / Azure AKS This project helped me move beyond basics and implement real DevOps practices closer to production environments 💪 #Kubernetes #DevOps #Docker #Minikube #ConfigMap #Secrets #SRE #CloudComputing #LearningByDoing

GitHub Actions - The Only Cheatsheet You Need I have been writing GitHub Actions workflows for years. And I still Google the same things every time. What was that expression syntax again? How do I cancel stale runs? Which action do I use for caching? So I put everything in one place. WHAT IS INSIDE 🔔 TRIGGERS — push, pull_request, schedule, workflow_dispatch, workflow_call, repository_dispatch and filters for branches, paths, tags and event types 📦 CONTEXT — every github.* and runner.* variable you actually use, plus steps, needs and job outputs ⚙️ JOBS & STEPS — needs, if conditions, timeout, environment gates, shell overrides, working directory 🔢 MATRIX BUILDS — parallel OS and version runs, include, exclude, fail-fast 🔐 SECRETS & VARIABLES — secrets vs vars, GITHUB_TOKEN, scope levels, passing secrets into reusable workflows ⚡ KEY ACTIONS — checkout, setup-node, cache, upload-artifact, docker build-push, AWS/Azure/GCP OIDC auth 🧮 EXPRESSIONS — contains, startsWith, toJSON, hashFiles, success(), failure(), always() 📤 OUTPUTS & ANNOTATIONS — GITHUB_OUTPUT, GITHUB_ENV, GITHUB_STEP_SUMMARY, masking, notice/warning/error 💻 GH CLI — run, rerun, watch, secrets, cache, all from terminal ONE THING MOST PEOPLE GET WRONG Pinning actions by tag is a supply chain risk. # risky  - uses: some-action@v1 # safe  - uses: some-action@a1b2c3d4 # full SHA A tag can be moved. A commit SHA cannot. Save this. Bookmark it. Send it to the person on your team who keeps asking you how to set up caching. Drop a comment if there is something missing and I will add it. #GitHubActions #DevOps #CICD #GitHub #DevSecOps #CloudNative #SoftwareEngineering #Automation #PlatformEngineering #100DaysOfCode