Cloud-native CI/CD Pipelines

Automated Cloud Deployment Pipeline: Golang Application to AWS ECS. A professional-grade project you can showcase on your resume and discuss confidently in interviews. Project Overview I recently implemented an enterprise-grade CI/CD pipeline that automates the deployment of containerized Golang applications to AWS ECS using GitHub Actions. This solution provides secure, scalable, and repeatable deployments with zero downtime. Key Technical Components 1. Security-First AWS Integration - Implemented IAM roles with least-privilege access principles - Created dedicated service accounts with scoped permissions: - ECR access for container management - ECS access for deployment orchestration - Minimal IAM read permissions for service discovery 2. Secure Secrets Management - Established encrypted GitHub repository secrets - Implemented short-lived credentials with automatic rotation - Separated deployment environments with distinct access controls 3. Container Registry Configuration - Configured private ECR repository with lifecycle policies - Implemented immutable image tags for deployment traceability - Set up vulnerability scanning for container images 4. Advanced CI/CD Workflow Automation - Designed multi-stage GitHub Actions workflow - Implemented conditional builds based on branch patterns - Created comprehensive build matrix for multi-architecture support - Integrated automated testing before deployment approval 5. Infrastructure Orchestration - Deployed ECS Fargate cluster with auto-scaling capabilities - Configured task definitions with resource optimization - Implemented service discovery and health checks - Set up CloudWatch logging and monitoring integration 6. Deployment Strategy - Implemented blue/green deployment pattern - Created automated rollback mechanisms - Established canary releases for production deployments - Set up performance monitoring during deployment cycles 7. Environment Management - Created isolated staging and production environments - Implemented approval gates for production deployments - Configured environment-specific variables and configurations - Established promotion workflows between environments 8. Validation and Monitoring - Integrated automated smoke tests post-deployment - Configured synthetic monitoring with alerting - Implemented deployment metrics collection - Created deployment dashboards for visibility Technical Skills Demonstrated - AWS Services: IAM, ECR, ECS, CloudWatch, Application Load Balancer - Docker container optimization and security - Infrastructure as Code principles - CI/CD pipeline engineering - Golang application deployment - Zero-downtime deployment strategies - Multi-environment configuration management Resume Impact Adding this project to your resume will: - Demonstrate hands-on experience with in-demand technologies (AWS, Docker, GitHub Actions) - Show your ability to implement end-to-end automation solutions -

A robust CI/CD pipeline is fundamental to streamlining your software delivery. We recently embarked on establishing a CI/CD pipeline for our team at LXA, and instead of the usual suspects (GitHub Actions, GitLab CI, Jenkins), we opted for GCP’s Cloud Build and Cloud Deploy. Here’s what we learned: Pros: • Serverless: No more managing VMs or clusters! • Enhanced Security: All build steps run within our GCP environment with support for granular service accounts. • Container-First: Native support for GKE/Kubernetes and Cloud Run. • Rapid Testing: Convenient build and deployment triggering without unnecessary commits. • Modern CD Workflow: Built-in support for releases, canaries, promotions, approvals, and rollbacks. • Cost-Effective: True pay-as-you-go pricing. Cons: • Fragmented Experience: Navigating between Cloud Build and Deploy can feel disjointed. • Git Integration: Better traceability with Git metadata (revisions, comments, PRs) would be really ideal. • Steep Learning Curve: Need to understand container and Kubernetes tooling, e.g. Docker, Skaffold, Kustomize. • Notifications: Surprisingly, setting up notifications/alerts is not user-friendly. Managing a CI/CD system can be challenging, especially at scale. Based on our experience so far, Cloud Build and Cloud Deploy seem to provide a good and comprehensive solution to run our CI/CD pipeline. --- Have you tried GCP’s CI/CD tools? Any learning you can share?

I’ve built CI/CD for 100+ cloud-native apps. And here’s the truth no one talks about… CI/CD for Kubernetes is NOT GitOps. This myth is slowing down your team. 𝐆𝐢𝐭𝐎𝐩𝐬 𝐢𝐬 𝐠𝐫𝐞𝐚𝐭 𝐟𝐨𝐫: → Infra changes with approvals. → Prod deploys where audit > speed. → Versioned, declarative workloads. 𝐁𝐮𝐭 𝐆𝐢𝐭𝐎𝐩𝐬 𝐛𝐫𝐞𝐚𝐤𝐬 𝐝𝐨𝐰𝐧 𝐰𝐡𝐞𝐧: → You need real-time deploys (dev/test). → You’re deploying non-K8s stuff (Lambdas, DBs, secrets). → You want fast feedback on feature branches. 𝐆𝐢𝐭𝐎𝐩𝐬 𝐢𝐬 𝐍𝐎𝐓 𝐂𝐈/𝐂𝐃. It’s just one flavor of CD. And it’s not always the right one. 𝐇𝐞𝐫𝐞’𝐬 𝐌𝐲 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤: (Hybrid CI/CD that actually works) 𝐆𝐢𝐭𝐎𝐩𝐬 ✓ Use ArgoCD or Flux for prod + infra. ✓ Works best when governance > agility. ✓ Enforce PR-based deploys + commit traceability. ✓ Lock down “who” changes “what” in YAML. 𝐏𝐮𝐬𝐡-𝐛𝐚𝐬𝐞𝐝 𝐂𝐃 (𝐧𝐨𝐧-𝐆𝐢𝐭𝐎𝐩𝐬) ✓ Use Tekton / Spinnaker / GitHub Actions for feature branches. ✓ Great for fast iterations + dynamic workloads. ✓ Ideal for ephemeral preview envs, blue/green, canary. ✓ Triggered by events not Git commits. 𝐁𝐨𝐭𝐡 𝐓𝐨𝐠𝐞𝐭𝐡𝐞𝐫 🫱🏼🫲🏼 ✓ ✓ GitOps handles stability. ✓ ✓ Push-CD handles speed. ✓ ✓ You get best of both: control + agility. The best DevOps teams I’ve worked with? They don’t pick GitOps or pipelines. They design around context. ♻️ 𝐑𝐄𝐏𝐎𝐒𝐓 𝐒𝐨 𝐎𝐭𝐡𝐞𝐫𝐬 𝐂𝐚𝐧 𝐋𝐞𝐚𝐫𝐧.

Interviewer: You have 2 minutes. Explain how a typical AWS CI/CD pipeline works. My answer: Challenge accepted, let’s do this. ➤ 𝐒𝐨𝐮𝐫𝐜𝐞 𝐒𝐭𝐚𝐠𝐞 It all starts when developers push code to a repository like GitHub or CodeCommit. This triggers a pipeline via a webhook or CloudWatch event. ➤ 𝐁𝐮𝐢𝐥𝐝 𝐒𝐭𝐚𝐠𝐞 AWS CodeBuild (or Jenkins on EC2) kicks in. It compiles the code, runs unit tests, lints the project, and creates build artifacts. These artifacts are pushed to S3 or an artifact store like ECR if we’re building Docker images. ➤ 𝐓𝐞𝐬𝐭 𝐒𝐭𝐚𝐠𝐞 Optional but powerful. You can run integration or security tests here. Think of tools like SonarQube, Trivy, or AWS Inspector. Fail fast, fix early. ➤ 𝐃𝐞𝐩𝐥𝐨𝐲 𝐒𝐭𝐚𝐠𝐞 Based on the environment (dev, staging, or prod), the pipeline uses AWS CodeDeploy, CloudFormation, or even CDK to deploy infrastructure and application code. For container-based apps, ECS or EKS handles deployments. For serverless, it's Lambda and SAM. ➤ 𝐑𝐨𝐥𝐥𝐛𝐚𝐜𝐤 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 Things break. Rollbacks are handled via deployment hooks, versioned artifacts, or blue-green/canary strategies in CodeDeploy or ECS. ➤ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐥𝐞𝐫𝐭𝐬 CloudWatch logs everything. Alarms can notify you via SNS or trigger rollbacks. X-Ray, Prometheus, and Grafana help trace and debug real-time issues. ➤ 𝐒𝐞𝐜𝐫𝐞𝐭𝐬 𝐚𝐧𝐝 𝐂𝐨𝐧𝐟𝐢𝐠 Secrets Manager or Parameter Store injects sensitive values safely at runtime. IAM roles ensure the least privilege across every stage. That’s your CI/CD pipeline in AWS—from code to production, automated, observable, and secure. Time’s up. Let's grow together.

Simplifying CI/CD in the Cloud with Azure + ArgoCD This diagram perfectly captures how modern cloud-native delivery works using Azure DevOps, Azure Kubernetes Service (AKS), and ArgoCD for GitOps. Here’s the flow: 1. Code to Container – A developer pushes code to Azure Repos. Azure DevOps triggers CI stages that build, push the Docker image to Azure Container Registry (ACR), and update Kubernetes manifests. 2. GitOps Takes Over – ArgoCD continuously watches the repo for manifest changes and automatically syncs those changes to AKS clusters. 3. Automated, Consistent Deployments – No more manual kubectl. Just clean, versioned, Git-based deployment. This kind of pipeline enables: • Full automation from commit to deploy • Rollback safety using Git history • Scalability and consistency for teams If you’re learning DevOps or building your own platform, this is the model to understand. Are you using GitOps in your environment? What tool are you pairing with Kubernetes? #DevOps #Azure #GitOps #ArgoCD #Kubernetes #CI/CD #CloudNative #AKS #InfrastructureAsCode

If you want to break into Cloud in 2025 - start by building these 3 real-world, cloud-native projects from the ground up. (also one GitHub repo you should definitely bookmark ) Most people sign up for free credits from cloud providers… but it's crucial to put them to meaningful use. Here’s your chance to stand out. 1. Full-Stack AWS CI/CD Pipeline Key components: → Infrastructure with Terraform (EC2, VPC, ECR) → Containerized applications with Docker → Automated deployments via GitHub Actions → EC2/Elastic Beanstalk deployment patterns → ECR integration + CloudWatch monitoring Tutorial Link: https://lnkd.in/d_5iFvqi Why this works: It shows the complete DevOps lifecycle - from infrastructure to monitoring. That's exactly what hiring managers look for. ------- 2. Kubernetes Delivery Pipeline on GCP Core elements: → Node.js/React application architecture → Container registry management (GCR) → GCP infrastructure with Terraform → GKE deployment patterns → GitHub Actions automation → Helm/kubectl orchestration Tutorial Link: https://lnkd.in/d3DN_dXS Why this works: You're showcasing containerized app deployment on managed Kubernetes - using enterprise-grade tools and patterns. ------- 3. Modern IaC with Pulumi (Azure/GCP) Project highlights: → Infrastructure as Code using Pulumi + JavaScript → CI/CD automation with GitHub Actions → Modern app deployment (React.js/Node.js) → Container orchestration with Kubernetes → Cloud-native service integration Tutorial Link: https://lnkd.in/dpFVjgSS Why this works: Pulumi demonstrates advanced IaC with actual programming logic - not just static YAML. That's what separates senior engineers from beginners. ------- Github Link with more such projects: https://lnkd.in/dh7WhvGU The Bottom Line, focus on: → Cloud-native architectural thinking → End-to-end deployment automation → Real-world GitOps & containerization → Production-ready operational skills Build projects that prove you understand how to deliver, deploy, and operate in cloud environments. • • • Found this useful? 🔔 Follow me (Vishakha Sadhwani) for more Cloud & DevOps insights ♻️ Share so others can learn as well

🚀 End-to-End DevOps Project on AWS I recently completed a cloud-native DevOps project where I built and deployed a full-stack application using Terraform, Jenkins, Docker and Kubernetes on AWS. 🔗 GitHub Repo: 👉 https://lnkd.in/g7G2Cd-v Here’s a breakdown of what I implemented: 🏗️ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗖𝗼𝗱𝗲 – 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 • Used Terraform to automate infrastructure provisioning with state management and locking enabled through AWS S3. ✅ Resources created: • VPC with 3 subnets: • Public Subnet → Bastion Host, VPN, ALB (Ingress Controller) • Private Subnet → EKS Cluster • DB Subnet → RDS (MySQL) • Integrated with Route53 (DNS), CDN, and EFS for persistent storage. ☸️ 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 – 𝗘𝗞𝗦 • Traffic enters through AWS ALB, handled by Ingress Controller • Routed to microservices via Kubernetes Services • Used Deployments, ConfigMaps, and Helm for management • Persistent data handled using EFS volumes via PVCs • Followed clean microservices architecture for separation of concerns 🚀 𝗖𝗜/𝗖𝗗 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 – 𝗝𝗲𝗻𝗸𝗶𝗻𝘀 • Set up a complete CI/CD pipeline triggered by GitHub webhooks. Jenkins pipeline includes: 1. Dependency installation 2. Code analysis with SonarQube 3. Infra provisioning using Terraform 4. Docker image build & push to Amazon ECR 5. Kubernetes deployment using Helm 📌 This project helped me understand the real-world DevOps workflow, from infrastructure setup to CI/CD automation and scalable deployments on EKS. 🔗 GitHub Repo: 👉 https://lnkd.in/g7G2Cd-v 🔁 Repost if you found it useful #AWS #DevOps #Terraform #Jenkins #EKS #Kubernetes #CICD #CloudComputing #InfrastructureAsCode #Helm #SonarQube #ECR #EFS #Route53 

🚀 End-to-End DevOps Project on AWS I recently completed a cloud-native DevOps project where I built and deployed a full-stack application using Terraform, Jenkins, Docker and Kubernetes on AWS. 🔗 GitHub Repo: 👉 https://buff.ly/mzzh2cf Here’s a breakdown of what I implemented: 🏗️ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗖𝗼𝗱𝗲 – 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 • Used Terraform to automate infrastructure provisioning with state management and locking enabled through AWS S3. ✅ Resources created: • VPC with 3 subnets: • Public Subnet → Bastion Host, VPN, ALB (Ingress Controller) • Private Subnet → EKS Cluster • DB Subnet → RDS (MySQL) • Integrated with Route53 (DNS), CDN, and EFS for persistent storage. ☸️ 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 – 𝗘𝗞𝗦 • Traffic enters through AWS ALB, handled by Ingress Controller • Routed to microservices via Kubernetes Services • Used Deployments, ConfigMaps, and Helm for management • Persistent data handled using EFS volumes via PVCs • Followed clean microservices architecture for separation of concerns 🚀 𝗖𝗜/𝗖𝗗 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 – 𝗝𝗲𝗻𝗸𝗶𝗻𝘀 • Set up a complete CI/CD pipeline triggered by GitHub webhooks. Jenkins pipeline includes: 1. Dependency installation 2. Code analysis with SonarQube 3. Infra provisioning using Terraform 4. Docker image build & push to Amazon ECR 5. Kubernetes deployment using Helm 📌 This project helped me understand the real-world DevOps workflow, from infrastructure setup to CI/CD automation and scalable deployments on EKS. 🔗 GitHub Repo: 👉 https://buff.ly/mzzh2cf 🔁 Repost if you found it useful hashtag#AWS hashtag#DevOps hashtag#Terraform hashtag#Jenkins hashtag#EKS hashtag#Kubernetes hashtag#CICD hashtag#CloudComputing hashtag#InfrastructureAsCode hashtag#Helm hashtag#SonarQube hashtag#ECR hashtag#EFS hashtag#Route53

Post 26: Real-Time Cloud & DevOps Scenario Scenario: Your organization is containerizing applications and deploying them via a CI/CD pipeline. However, a recent security incident occurred because a container image with known vulnerabilities was pushed to production. This exposed critical data and forced an emergency patch. As a DevOps engineer, your task is to integrate security scanning into the CI/CD workflow—often called "shifting left" on security—to prevent vulnerable images from reaching production. Step-by-Step Solution: Set Up Automated Image Scanning: Integrate tools like Trivy, Aqua Security, or Anchore in the CI pipeline to scan container images before they’re pushed to a registry. Fail the build if any high or critical vulnerabilities are detected. Use a Secure Base Image: Choose minimal, well-maintained base images (e.g., Alpine, Distroless) to reduce the attack surface. Keep images updated by regularly pulling the latest base versions. Implement Policy-Driven Pipeline Gates: Define security policies to block images with known critical CVEs (Common Vulnerabilities and Exposures).Enforce these policies in your CI/CD pipeline using scripts or plugins. Example (GitHub Actions or Jenkins): yaml Copy steps: - name: Run Trivy Scan run: | trivy image --exit-code 1 --severity HIGH,CRITICAL my-image:latest Leverage SBOM (Software Bill of Materials): Generate an SBOM for each image to track dependencies and their versions. This helps quickly identify which images are affected by newly disclosed vulnerabilities. Adopt Role-Based Access Control (RBAC): Restrict permissions in your container registry and CI/CD tooling. Ensure only authorized users and pipelines can push images to production repositories. Regularly Update Dependencies: Automate dependency checks in your Dockerfiles and application code. Use tools like Dependabot, Renovate, or native build tools to keep libraries current. Perform Ongoing Monitoring and Alerts: Continuously monitor container images in production for newly disclosed vulnerabilities. Send automated alerts if newly discovered issues are found in active images. Establish a Quick Response Process: Define procedures for patching and redeploying affected images. Maintain an incident response plan to minimize downtime if a vulnerability slips through. Outcome: Improved security posture by preventing vulnerable images from reaching production. Reduced risk of exposing critical data, thanks to early detection and remediation. 💬 How do you integrate security scanning in your container workflows? Share your strategies below! ✅ Follow Thiruppathi Ayyavoo for daily real-time scenarios in Cloud and DevOps. Let’s evolve and secure our pipelines together! #DevOps #CloudComputing #SecurityScanning #ContainerSecurity #CI_CD #ShiftLeft #RealTimeScenarios #CloudEngineering #TechSolutions #LinkedInLearning #careerbytecode #thirucloud #linkedin #USA CareerByteCode