CI/CD Pipeline for Automated Portfolio Deployment with GitHub Actions and AWS S3

🚀 Built a Production-Ready Terraform Project on AWS (Real DevOps Implementation) As part of strengthening my DevOps expertise, I designed and deployed a modular Terraform project to provision AWS infrastructure — following real-world practices used in organizations. Instead of writing everything in a single file, I implemented a scalable and reusable architecture using Terraform modules 👇 🏗️ What I built: ✔️ VPC with public subnet ✔️ Internet Gateway & Route Table configuration ✔️ Security Group (SSH & HTTP access) ✔️ EC2 instance deployment 📁 Project Approach (Industry-Level): 🔹 Separate modules for VPC and EC2 🔹 Environment-based structure (dev) 🔹 Clean and maintainable code design 💡 Real Challenges I Solved (Hands-on Debugging): 🔸 Fixed invalid AMI issue (region-specific problem) 🔸 Resolved instance type restriction (Free Tier eligibility) 🔸 Handled Git large file error by cleaning .terraform and using .gitignore 👉 These are the exact issues you face in real production environments. 📌 Key Learnings: ✔ Modular Terraform = scalable infrastructure ✔ Proper Git practices are critical in DevOps ✔ Debugging skills matter more than just writing code 🔗 GitHub Project Link: https://lnkd.in/d4JKWgGE #DevOps #Terraform #AWS #InfrastructureAsCode #CloudEngineering #SRE #GitHub #LearningInPublic

🚀 How Terraform Reduced Our Infrastructure Provisioning Time by 70% Provisioning cloud infrastructure manually through the AWS console can become slow, inconsistent, and difficult to scale as environments grow. While managing multiple environments (Dev, QA, and Prod), we noticed that setting up infrastructure manually was taking hours and sometimes even days. To solve this, we implemented Infrastructure as Code using Terraform. ⚠️ Challenges Before Terraform • Manual creation of AWS resources (VPC, EC2, Load Balancer, Security Groups) • Configuration inconsistencies across environments • Slow environment setup for testing and deployments • Difficult rollback and change tracking ⚙️ Terraform-Based Workflow Developer → Git Repository → Terraform Code → Terraform Plan → Terraform Apply → AWS Infrastructure Provisioned ☁️ Resources Automated • VPC and networking • EKS cluster • Application Load Balancer • IAM roles and policies • RDS database • Security groups 📊 Results ✅ 70% faster infrastructure provisioning ✅ Consistent and repeatable deployments ✅ Faster environment setup for development teams ✅ Infrastructure fully version-controlled using Git 💡 Key takeaway: Infrastructure as Code transforms infrastructure management into an automated, scalable, and reliable process. What tools are you using for Infrastructure as Code in your environment? #DevOps #Terraform #AWS #InfrastructureAsCode #CloudEngineering #Kubernetes #PlatformEngineering

🚀 Built my first real-world AWS infrastructure using Terraform Not just tutorials. Not copy-paste. I wanted to understand how real DevOps teams design infrastructure — so I built it from scratch 👇 🔹 Designed a VPC with public subnet + EC2 deployment 🔹 Configured Security Groups (SSH, HTTP, custom rules) 🔹 Implemented remote state using S3 + DynamoDB (state locking 🔐) 🔹 Broke everything into reusable Terraform modules 🔹 Applied Infrastructure as Code principles end-to-end Biggest learning? 👉 Writing Terraform is easy 👉 Designing scalable, reusable infrastructure is the real skill I also got hands-on with: • State management & locking • Variables & environment structuring • Terraform CLI workflows (init, plan, apply, destroy) • Debugging real infra issues Now moving towards: ➡️ CI/CD pipelines with Terraform ➡️ Kubernetes (EKS) deployments ➡️ Multi-environment (dev/qa/prod) setups This is just the start — building towards production-level DevOps 🚀 #DevOps #Terraform #AWS #InfrastructureAsCode #CloudEngineering #CI_CD

I'm excited to share one of my recent cloud engineering projects: CloudTask Pro — a production-grade task management platform deployed on AWS. The goal of this project was not only to build a web application, but to design and deploy it using a realistic production-style cloud architecture and DevOps workflow. 𝗞𝗲𝘆 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁:  • Infrastructure as Code using Terraform modules  • CI/CD pipeline with Jenkins and GitHub webhooks  • Dockerized backend deployment with Docker Hub  • Frontend hosting using Amazon S3 and CloudFront  • Backend deployment on EC2 Auto Scaling Group behind an Application Load Balancer  • PostgreSQL database hosted on Amazon RDS  • Secrets management with AWS Secrets Manager  • Monitoring and logging using CloudWatch  • Public/private subnet separation inside a custom VPC  • Internal deployment automation using AWS Systems Manager (SSM) 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝘂𝘀𝗲𝗱:  • VPC, public/private subnets, route tables, NAT Gateway  • EC2, Auto Scaling Group, Launch Template  • Application Load Balancer  • Amazon RDS PostgreSQL  • Amazon S3 and CloudFront  • IAM roles and security groups  • Jenkins, Docker, GitHub, Terraform 𝗙𝗼𝗿 𝗱𝗲𝘁𝗮𝗶𝗹𝘀, 𝗽𝗹𝗲𝗮𝘀𝗲 𝗰𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝘁𝗵𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗿𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆: https://lnkd.in/d6fiEi3m #AWS #DevOps #Terraform #Jenkins #Docker #CICD #CloudEngineer #AWSSolutionsArchitect #InfrastructureAsCode 

🚀 Built an Automated CI/CD Pipeline with AWS & GitHub Actions I recently completed a hands-on project where I designed and implemented a complete CI/CD pipeline to deploy a Node.js application on AWS EC2. 🔧 What I built: * A Node.js application running on port 4000 * AWS EC2 (Ubuntu) server setup with proper security groups * Automated deployment using GitHub Actions * Secure SSH-based connection using GitHub Secrets * Process management using PM2 ⚙️ How it works: Every time I push code to the main branch, GitHub Actions automatically: 1. Connects to my EC2 instance via SSH 2. Pulls the latest code 3. Installs dependencies 4. Restarts the application 💡 Challenge I faced: I initially ran into a “Permission denied (publickey)” error during deployment. After debugging, I resolved it by correctly adding the CI/CD SSH key to the EC2 server and setting proper permissions. This was a great real-world troubleshooting experience. 📂 GitHub Repository: https://lnkd.in/g8qMusNS 🎯 Key Takeaways: * Hands-on experience with CI/CD pipelines * Understanding secure SSH-based automation * Debugging real production-like issues * Automating deployments with zero manual intervention This project helped me strengthen my DevOps and SRE skills by working on real-world deployment scenarios. #DevOps #AWS #GitHubActions #CI_CD #SRE #CloudComputing #NodeJS #Automation

🚀 Built & Deployed My DevOps Portfolio on AWS (Live Project) Excited to share that I have successfully deployed my personal DevOps portfolio using AWS S3 — and it’s LIVE now! 🔥 🌐 Live Website: https://lnkd.in/d4_detHr --- 🌐 **Project 1: Static Website Hosting on AWS S3** ✅ Designed and deployed my personal DevOps portfolio ✅ Enabled static website hosting using S3 ✅ Configured bucket policies for public access ✅ Handled “Access Denied” issues by debugging IAM & bucket policies 💡 Learned: * Difference between IAM vs Bucket Policy * How S3 serves static content over HTTP * Public access control & security best practices --- 🔐 **Project 2: Secure S3 Access Control (IAM + Bucket Policy)** ✅ Created IAM users with different access levels ✅ Attached S3 permissions using IAM policies ✅ Implemented bucket policy to **restrict access even when IAM allows it** ✅ Tested real-world scenario using multiple users --- 📚 Key AWS S3 Concepts I Understood: 🔹 S3 is highly scalable object storage 🔹 Provides **99.999999999% durability (11 nines)** 🔹 Stores any type of data (logs, backups, static files) 🔹 Data is replicated across multiple AZs 🔹 Used in real-world for hosting websites & storing application data --- 🎯 **Real Learning** Faced multiple errors like: ❌ Access Denied ❌ Invalid Resource in policy * 🔥 Explicit DENY overrides ALLOW * Fine-grained access control in AWS * Real-world security implementation ➡️ Solved them by understanding how AWS permissions actually work. -------- 💬 Learning DevOps by building is a game changer. #DevOps #AWS #S3 #Cloud #LearningInPublic #Terraform #Docker #Kubernetes #DevOpsEngineer

Built and automated AWS infrastructure using Terraform — from event-driven monitoring to a serverless API ☁️🚀 Over the past few weeks, I’ve been working on two cloud projects focused on Infrastructure as Code and DevOps automation. 🔹 EC2 State Change Monitoring System 🔹 Serverless API (API Gateway + Lambda + DynamoDB) Instead of treating these as separate builds, I focused on end-to-end system design and automation. ⚙️ What was implemented Event-driven monitoring using EventBridge → Lambda → SNS API-based backend using API Gateway → Lambda → DynamoDB Infrastructure defined and deployed using Terraform CI/CD pipeline using GitHub Actions 🧠 Where Terraform fits Terraform acted as the control layer, handling: Infrastructure provisioning Service dependencies Lambda packaging (archive_file) Consistent deployments across environments 🔄 DevOps workflow Code → GitHub → CI/CD → Terraform → AWS Remote state stored in S3 State locking via DynamoDB Automated deployments from GitHub 🔍 Key troubleshooting lessons Fixed CI/CD failures caused by malformed AWS credentials Resolved Lambda runtime issues using CloudWatch logs Handled Terraform state conflicts by implementing remote backend Debugged resource duplication due to missing shared state Corrected handler and packaging issues in Lambda 🔥 Key takeaway Building infrastructure is one thing — making it automated, consistent, and reliable is what defines DevOps. #Terraform #AWS #DevOps #AWS #Terraform #DevOps #CloudComputing #InfrastructureAsCode #Serverless #GitHubActions #CloudEngineering #DevOpsEngineer #BuildInPublic

Following up on my last post, here’s how the system actually works. In my previous post, I shared the challenges I faced while building a DevOps pipeline and how I almost gave up before finally getting it to work. Now I want to break down what I actually built and how the system works end-to-end. What the setup does •Provisions an EC2 instance on AWS using Terraform •Configures security groups (SSH + HTTP access) •Installs Docker automatically using user data •Deploys an Nginx container on port 80 •Uses GitHub Actions to automate the entire workflow. What happens on every push •Code is pushed to GitHub •GitHub Actions pipeline is triggered •Terraform initializes and plans infrastructure •EC2 instance is created or updated •Docker is installed automatically •Nginx container is deployed and exposed on port 80. Architecture flow GitHub → GitHub Actions → Terraform → AWS EC2 → Docker → Nginx What this helped me understand After fixing all the failures I mentioned earlier, seeing this flow work end-to-end made things clearer: •How CI/CD connects directly to infrastructure •How Terraform manages cloud resources •How automation removes manual setup •How small misconfigurations can break the entire pipeline. I’m continuing to improve this setup and explore better ways to structure and scale it. Open to feedback and suggestions though. #DevOps #AWS #Terraform #Docker #CI/CD #GitHubActions #InfrastructureAsCode #CloudComputing

🚀 Designed and Implemented a Production-Style CI/CD Pipeline Across AWS Services Recently worked on building a CI/CD pipeline designed to mirror real-world production workflows, focusing on automation, reliability, and repeatability. 🔧 Core Technologies Used: • Jenkins & GitHub Actions — CI/CD automation • Docker — Containerized application workloads • Terraform — Infrastructure provisioning (IaC) • AWS EC2 — Hosted CI infrastructure • AWS ECS — Managed container deployments • AWS EKS — Kubernetes-based orchestration • Azure — Multi-cloud workflow exposure • GitHub — Version control and pipeline triggers 📌 Key Focus Areas: ✔ Infrastructure as Code for repeatable environments ✔ Automated build → test → deploy workflows ✔ Container lifecycle management across ECS and EKS ✔ Pipeline reliability and failure handling ✔ Cross-service debugging and observability One takeaway from this build: Most pipeline failures aren't caused by major issues — they're usually small configuration mismatches that cascade across services. Designing resilient pipelines requires thinking beyond deployment — it’s about building systems that can recover, log clearly, and scale predictably. 💬 Curious to hear from others in the field: What’s one practice you consider non-negotiable when designing production CI/CD pipelines? #DevOps #AWS #Terraform #Docker #GitHubActions #Jenkins #Kubernetes #EKS #CI_CD #CloudEngineering