Pancadrya Portfolio | Cloud & AI

📌 Project Description

In the modern software development lifecycle, manually uploading files to production servers is an error-prone and inefficient practice. This project demonstrates the implementation of DevOps principles by building a fully automated CI/CD pipeline architecture using native AWS developer tools.

The primary objective of this project is to centralize source code into a secure repository and orchestrate an automated pipeline that seamlessly deploys code changes directly to the production environment (Amazon S3 & CloudFront) with zero downtime.

🛠️ Tech Stack & AWS Services

Developer Tools: AWS CodeCommit (Private Git Repository), AWS CodePipeline.
Storage & Content Delivery: Amazon S3 (Static Website Hosting & Artifact Store), Amazon CloudFront.
Local Environment: Git, AWS CLI, Visual Studio Code (VS Code).
Concepts: Continuous Integration / Continuous Delivery (CI/CD), Version Control System (VCS), Infrastructure as Code (JSON Pipeline Configuration).

🏢 Business Scenario

A mid-sized business manages a public-facing website that requires frequent content updates. Previously, the team had to manually upload HTML/JS files to Amazon S3 for every change, causing collaboration bottlenecks and a lack of code version visibility. To support team scalability and deployment reliability, a CI/CD pipeline was engineered. This solution empowers multiple developers to collaborate on a single codebase securely, ensuring that every commit pushed to the main branch is automatically deployed to production.

🚀 Implementation Steps

Phase 1: Version Control Setup (Source)

Provisioned a highly secure, private Git repository using AWS CodeCommit (front_end_website).
Cloned the repository to the local development environment (VS Code) utilizing the HTTPS (GRC) protocol.
Executed the initial commit to establish the repository’s foundational directory structure.

CI/CD Pipeline 1

Phase 2: CI/CD Pipeline Configuration (Deploy)

Provisioned the AWS CodePipeline architecture programmatically via the AWS CLI using a declarative JSON configuration (cafe_website_front_end_pipeline.json).
Source Stage: Integrated the pipeline to track changes on the main branch of the CodeCommit repository.
Deploy Stage: Configured Amazon S3 as the deployment target. Output artifacts are automatically extracted and pushed to the production bucket.
Injected Cache-Control metadata (max-age=14) into the pipeline deployment configuration to ensure seamless integration with Amazon CloudFront edge caching.

Phase 3: Git Integration & Local Workflow

Established a developer workflow utilizing the VS Code Git integration.
Migrated the local production website assets into the local repository directory.
Staged, committed, and pushed the updated codebase to the upstream CodeCommit repository via the terminal.

Phase 4: Automated Deployment Validation

Monitored the AWS CodePipeline visualizer to confirm that the Git push successfully triggered an automated pipeline execution.
Verified the live deployment by accessing the Amazon CloudFront distribution domain.
Utilized browser developer tools to inspect HTTP headers, successfully validating the Cache-Control: max-age=14 implementation deployed by the pipeline.

CI/CD Pipeline 2

🎯 Results & Key Takeaways

Full Automation (Zero-Touch Deployment): Successfully eliminated manual deployment processes. A simple git push command now updates the production website in seconds.
Centralized Collaboration (Version Control): Enabled robust revision tracking, code auditing, and instant rollback capabilities by migrating source control to AWS CodeCommit.
Optimized Content Delivery: Automated caching metadata injection within the pipeline ensures that the S3 storage and CloudFront CDN integration performs optimally without manual intervention.