DevOps & Cloud Engineering Projects

Hands-on DevOps and Cloud engineering projects demonstrating CI/CD automation, cloud-native Kubernetes deployments, containerization, and Infrastructure as Code.

(Built while learning, experimenting, breaking things, fixing them, and occasionally wondering why it didn’t work the first time.)

Docker Web Application Deployment

Containerization fundamentals demonstrated through building, running, and distributing Docker images.

View Steps

  • Build custom Docker images for a static web application.
  • Run containers locally and expose services via browser.
  • Create Docker Compose setup for web and database services.
  • Push and pull images from Docker Hub.

CI/CD Automation with GitHub Actions

Automated CI/CD pipelines for application build, test, and deployment using GitHub Actions.

View Steps

  • Create GitHub repositories and workflow definitions.
  • Automate builds and deployments for a Node.js application.
  • Implement CI and CD stages targeting cloud environments.

Cloud Architecture & Serverless Systems

Designing highly available, fault-tolerant, and serverless cloud architectures.

View Steps

  • Design multi-AZ cloud architectures.
  • Implement load balancing and auto-scaling.
  • Build serverless applications using AWS Lambda or Azure Functions.
  • Use managed NoSQL databases for scalability.

Infrastructure Automation with Terraform & Ansible

Infrastructure as Code project automating provisioning and application deployment.

View Steps

  • Provision virtual machines using Terraform.
  • Parameterize infrastructure using variables.
  • Deploy Prestashop using Ansible automation.

Drupal High-Availability Cloud Platform

Enterprise-style Drupal deployment using Terraform, Ansible, Docker, Kubernetes, and CI/CD.

View Steps

  • Provision multi-AZ infrastructure with Terraform.
  • Configure Drupal servers with Ansible.
  • Optional containerization and Kubernetes deployment.
  • CI/CD pipelines with GitHub Actions.
  • Monitoring with Prometheus and Grafana.

Serverless Image Processing Pipeline (AWS)

Event-driven cloud-native image processing using AWS Lambda, S3, Docker, and CloudWatch.

View Steps

  • S3-triggered Lambda execution.
  • Image resizing with Python and Pillow.
  • IAM least-privilege configuration.
  • Logging and monitoring with CloudWatch.

Cloud-Native DevOps CI/CD Platform on Kubernetes

End-to-end DevOps CI/CD platform demonstrating fully automated Docker image builds for backend and frontend services, followed by automated cloud-native Kubernetes deployments with versioned rolling updates triggered by Git commits.

View Steps

  • Develop backend and frontend services using Python (FastAPI).
  • Containerize applications with Docker.
  • Deploy to Kubernetes (k3s) using Deployments and Services.
  • CI pipelines with GitHub Actions and image pushes to GHCR.
  • Continuous Deployment using a self-hosted GitHub Actions runner.
  • Immutable image versioning using full Git commit SHA.
  • Rolling updates with zero downtime.
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Training Myself

I have little real-time experience; therefore, I am trying to have additional practice fun in my free time...

Docker Web App Deployment

This project demonstrates building and deploying a custom Docker image for a simple web application, and pushing it to Docker Hub.

View Steps

  • Install Docker and build a custom image for a static HTML page.
  • Run the container and access the app via browser.
  • Create a Docker Compose file for a web and DB container (e.g., MySQL/PostgreSQL).
  • Push the image to a Docker Hub repository.
  • Pull the image on a new server and access it in the browser.

CI/CD Pipeline with GitHub Actions

This project focuses on automating CI/CD for a Node.js application using GitHub Actions and deploying it to a cloud provider.

View Steps

  • Create a GitHub repository and fork a Node.js web app.
  • Set up GitHub Actions to automate deployment to AWS, Azure, or GCP.
  • Build both a CI (test/build) and CD (deploy) pipeline.

Cloud Architecture & Serverless

Exploring high availability, fault tolerance, and serverless computing using AWS or Azure.

View Steps

  • Design a multi-AZ architecture using subnets across zones.
  • Implement load balancing and auto-scaling.
  • Create a serverless app with AWS Lambda or Azure Functions triggered by S3/HTTP events.
  • Use a serverless database like DynamoDB or Cosmos DB.

Terraform and Prestashop Automation

Automating infrastructure provisioning and Prestashop deployment using Terraform and Ansible.

View Steps

  • Install Terraform locally.
  • Create a Terraform config to provision a VM.
  • Use variables for size and region.
  • Deploy Prestashop with DB using Terraform and Ansible.

Drupal Cloud Infrastructure

High availability Drupal deployment using Terraform, Ansible, Docker, Kubernetes, and CI/CD tools.

View Steps

  • Provision infrastructure across multi-AZ using Terraform (VMs, RDS, ALB, security, VPC).
  • Configure Drupal app servers with Ansible (PHP, Apache, DB, rsync).
  • Optional: Containerize with Docker, use Compose locally, deploy via Kubernetes.
  • Create CI/CD pipelines with GitHub Actions (build, test, deploy to staging & prod).
  • Implement centralized logging with ELK or cloud-native options.
  • Monitor with Prometheus + Grafana (CPU, memory, DB, LB, alerts).
  • Design infrastructure for HA and disaster recovery with backups, auto-scaling.

Serverless Image Processing Pipeline (AWS Lambda + S3)

Automated, event-driven image thumbnail generation using AWS Lambda, S3, Python, Docker, and CloudWatch.

View Steps

  • Design an event-driven architecture using S3 object upload triggers.
  • Develop a Python AWS Lambda function for image resizing using Pillow.
  • Build AWS-compatible Lambda dependencies with Docker and SAM build images.
  • Implement a multi-bucket workflow for original images and generated thumbnails.
  • Configure IAM roles with least-privilege permissions for secure S3 access.
  • Enable observability using CloudWatch Logs for debugging and monitoring.
  • Optimize performance using Lambda memory tuning and ephemeral storage.
  • Deliver a production-ready, scalable image-processing pipeline for real-world workloads.