Automate Java Web App Deployment with AWS CodeDeploy

Yay, that's Day #5 of the 6 Day DevOps Challenge ticked off! ✔️ Here's how you can automate the deployment of your Java web app using AWS CodeDeploy. I just did this in a project: ✅ Launched an EC2 instance and configured networking with CloudFormation. ✅ Wrote bash scripts to set up, start, and stop my app on an EC2 instance. ✅ Created an appspec.yml file, transforming my manual scripts into a structured CodeDeploy workflow. ✅ Created a CodeDeploy application and deployment group, turning my scripts into a fully automated, one-click deployment process. 📸 Check out the documentation below for my project steps and learnings. 🙏 Thanks to NextWork for their support and the 6 Day DevOps Challenge. https://lnkd.in/d7yCPtAR #AWS #CloudComputing #DevOps #CodeDeploy #6DayDevOpsChallenge #NextWork

🚀 Day #3 of the 7-Day DevOps Challenge 🚀 Just learned to secure Java dependencies with AWS CodeArtifact! 🗃️ In this project, I: ✅ Created a web app using Maven and pushed it to GitHub. ✅ Set up a CodeArtifact repository to manage dependencies. ✅ Configured Maven to fetch dependencies securely through CodeArtifact. ✅ Created and attached IAM policies to securely access CodeArtifact. In this project, I've learned how to: 🗂️ Set up and configure AWS CodeArtifact as a private Maven repository for managing dependencies. 🛡️ Use IAM roles and policies to let your EC2 instance access CodeArtifact. ✅ Verify your web app's connection to CodeArtifact and ensure Maven can download dependencies from it. 💎 Create and add your own packages to your CodeArtifact repository This project gave me hands-on experience with securing development dependencies and taught me how to build more secure Java applications. Check out my documentation below to see how I did it all from scratch. 🙏 🔥 Keep me accountable! My next post will be about Day #4 of the 7 Day DevOps Challenge, where I'll automate my web app's build using AWS CodeBuild. 🙏 Big Thanks to NextWork and Shoutout to Natasha Ong for the 7 Day DevOps Challenge and for being an amazing instructor. https://lnkd.in/gWWJEaJV #codeartifact #IAM #devops #cicd #7DayDevOpsChallenge #NextWork

Deploying a backend is one thing… making it reliable is another After setting up my VPS, Docker containers, Nginx, and HTTPS, I thought I was done. But then I realized something serious… If my server crashes, I lose everything. So I took it a step further and implemented: ~ Automated MongoDB backups ~ Shell scripting (bash) ~ Cron jobs for scheduling ~ Backup compression for storage efficiency One concept that stood out for me: Cron jobs. They allow you to schedule tasks like: “Run this script every day at 2AM” That means my database now backs itself up automatically no manual work. This experience taught me that: It’s not just about building systems… It’s about making them resilient. If you're learning backend or DevOps, don’t stop at deployment. Think reliability. #DevOps #BackendDevelopment #Docker #LearningInPublic #SoftwareEngineering

Headline: Abhishek Veeramalla DevOps, Automation & Infrastructure Docker, Inc Amazon Web Services (AWS)🚀 Bridging the Gap: From Django Development to AWS Deployment with Docker I’m excited to share my latest project—a fully containerized Django web application deployed on AWS EC2. As an SDET, I believe that understanding the deployment lifecycle is crucial for building robust automation frameworks. Key Highlights of the Project: Containerization: Built a custom Docker image from an Ubuntu 22.04 base to ensure environment parity. Orchestration: Managed container lifecycles, port mapping (8000:8000), and background execution using Docker detached mode. Infrastructure: Deployed on an AWS EC2 instance, handling security groups and inbound traffic rules. Documentation: Created a "Golden Path" workflow in the README to prevent common pitfalls like port conflicts and build context errors. This project sharpened my skills in Linux terminal operations, Docker Hub registry management, and Git version control. Check out the repository here: https://lnkd.in/ghBNCsSr #SDET #DevOps #Docker #AWS #Django #AutomationEngineering #ContinuousDeployment #BengaluruTech

Backend gave me the systems. DevOps is how I learn to run them.👨💻📈😎🤘 I'm making the move into DevOps, the backend work I've done is exactly what pushed me here. Over the past few months I built five production-grade backend systems from scratch. $ An Auth Service with refresh token rotation and theft detection. A distributed background job processor with DLQ, idempotency, and graceful shutdown. An Inventory Management Service. An event-driven Notification Service with per-channel failure isolation. A standalone Rate Limiter that three other services call Token Bucket, Sliding Window, Fixed Window, all backed by Redis Lua scripts. $ Each one has Docker Compose. Structured logging with Pino. Prometheus metrics. Health checks. GitHub Actions CI. Here is the thing though. I built all of that and realised I had never actually seen any of it run in production. The metrics endpoints exist. Nothing scrapes them. The CI pipelines test on every push. Nothing deploys when they pass. The health checks are there. No load balancer is watching them. That's not a criticism of the work but it's just where the natural next question lives.😁 I understand how these systems behave. I want to understand how they run. Linux, real server deployments, Nginx, CI/CD that ships, Prometheus and Grafana completing the loop I already started. Kubernetes once I've earned the right to need it. Backend gave me the systems. DevOps is how I learn to operate them. If you're on a similar path or already working in this space — I'd genuinely love to hear what you wish you'd known earlier. #DevOps #Linux #Production #Deployment

As a DevOps-focused Infrastructure Specialist, I recently completed a hands-on project where I containerized a Django application using Docker. 🔧 What I implemented: Built and tagged a custom Docker image (python:3.9) Designed an optimized Dockerfile (layering, caching, reduced image size) Managed dependencies using requirements.txt Created a portable and reproducible runtime environment 💡 Key DevOps concepts applied: Containerization & environment isolation Docker image lifecycle & optimization Reproducible builds for consistent deployments Foundation for CI/CD pipelines and Kubernetes orchestration 🎯 Why this matters: Containerization is a critical skill for modern cloud-native development. This project reflects practical experience in building scalable, deployment-ready applications—aligned with industry needs in every cloud and infrastructure landscape. 🧠 Currently expanding into: Kubernetes (K8s) orchestration CI/CD pipelines (GitHub Actions / GitLab CI) Cloud platforms #DevOps #CloudEngineering #Docker #Kubernetes #AWS #Linux #Infrastructure #SRE #CI_CD #CloudNative #GermanyJobs #TechCareers

I want to share a hard-earned lesson from my DevOps journey: ignoring .NET dependency updates can seriously jeopardize your application’s stability and security. My C# app built fine initially, but over time obscure bugs, build failures, and security warnings started hurting our delivery. To tackle this, I integrated dependency update tools like Dependabot into our Azure DevOps & GitHub Actions pipelines, scheduled regular update mini-sprints, and implemented regression testing automation. These changes improved our deployment stability and reduced technical debt. If you’re managing pipelines with .NET or similar stacks, consider adopting these best practices to maintain quality and reduce risk. Feel free to connect and discuss how you manage dependency updates! #DevOps #DotNet #AzureDevOps #DependencyManagement

-: A YAML workflow defines an automated CI pipeline in GitHub Actions. It specifies when the pipeline runs (event), what tasks to perform (jobs), and the sequence of actions (steps) executed on a runner. -: A YAML workflow defines when to run, what jobs to execute, and the sequence of steps inside each job. on: push jobs:  build:   steps:    - checkout    - setup-dotnet    - build    - run ----------------------- 👉Event → push Job → build Steps → checkout, build, run #git #githubaction #cicd #dotnetcore #azure #devops #webdevelopement #developers

🚀 Why Docker Is Important for Every Developer Let me be honest. Early in my career, “it works on my machine” was my biggest enemy. Different environments, dependency issues, version mismatches… chaos. Then I started using Docker. Everything changed. Here’s why Docker actually matters in real projects 👇 🔹 1. Consistent Environments Same app, same container, runs anywhere — local, QA, prod. No more “it works on my machine” excuses. 🔹 2. Faster Onboarding New developer joins? Just run docker-compose up → project is ready. No 2-day setup headaches. 🔹 3. Perfect for Microservices In modern systems (Java + Spring Boot + Kafka + DBs), Docker lets you spin up everything locally in minutes. 🔹 4. Smooth CI/CD Deployments With Docker images, deployments become predictable. What you test is exactly what goes to production. 🔹 5. Isolation & Stability Each service runs in its own container. No dependency conflicts, no breaking other services. 🔹 6. Works Seamlessly with Kubernetes If you're using Kubernetes (EKS/AKS), Docker is the foundation. No Docker → no real cloud-native system. 💡 From my experience: Using Docker + Kubernetes reduced deployment issues drastically and improved team productivity. 👉 If you’re a developer and not using Docker yet, you’re making your life harder than it needs to be. 💬 Curious — what was your biggest “it works on my machine” moment? #Docker #Microservices #DevOps #Java #SpringBoot #Kubernetes #Cloud #SoftwareEngineering

Wrapped up building a complete CI/CD pipeline on AWS — taking a Java web application from GitHub commit to a live production server, fully automated with a human approval gate. The stack: 🔹 GitHub (source control, SSH-authenticated) 🔹 AWS CodeBuild (Maven build, artifact packaging) 🔹 Amazon S3 (build artifact storage with lifecycle rules) 🔹 AWS CodeDeploy (EC2 in-place deployment with the CodeDeploy agent) 🔹 AWS CodePipeline (orchestration across all stages) 🔹 Amazon SNS (email notifications for the approval gate) 🔹 Amazon EC2 (separate dev and prod Ubuntu servers running Tomcat) 🔹 AWS IAM (service roles, managed policies, and an admin IAM user) The flow: push code to GitHub → CodePipeline detects the change → CodeBuild compiles the WAR → pipeline pauses for manual approval via SNS email → CodeDeploy ships it to the prod server → the new version is live on port 8080. What made it real was the troubleshooting along the way: • A cloned Git repo that carried a stale remote pointing at a different service — had to wipe `.git` and re-initialize against the correct origin • macOS metadata files (`._*`, `.DS_Store`) polluting a tarball extracted on Linux — fixed with rsync exclude patterns • AWS console UI drift from older tutorial instructions (CodeBuild's OAuth flow and CodePipeline's new "Build custom pipeline" entry point) • IAM users being blocked from billing actions even with AdministratorAccess — a good reminder that root-user actions are a separate privilege boundary • A build image mismatch (corretto11 vs corretto17) that surfaced a YAML runtime error — fixed directly in GitHub and re-triggered the pipeline End-to-end, this exercise reinforced that a pipeline isn't just the happy path. Real DevOps work is in the edges: permission boundaries, credential hygiene, environment drift, and the discipline to debug rather than start over. On to the next one. https://lnkd.in/gdGuyu3G #AWS #DevOps #CICD #CodePipeline #CodeBuild #CodeDeploy #CloudComputing