// ENGINEERING CASE STUDY 01

Achieving 99.99% Reliability
with Automated Pipelines

Migrating a fragile manual deployment process to a robust Zero-Downtime architecture using Docker, GitHub Actions, and Prometheus.

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The Challenge

A rapidly growing SaaS startup was facing critical stability issues. Deployment was a manual, error-prone process that took hours, leading to developer fatigue and customer dissatisfaction due to frequent outages.

Fragile Deployment Process

Manual FTP uploads and script execution caused inconsistent environments between Staging and Production.

High MTTR (Mean Time to Recovery)

Rollbacks were manual and complex, taking up to 45 minutes to restore service after a bad deploy.

Lack of Observability

Zero visibility into server health. Crashes were often reported by users before engineers knew.

user@prod-server:~$ git pull origin master

> Unpacking objects: 100% (45/45), done.

user@prod-server:~$ npm install

> added 142 packages in 8s

user@prod-server:~$ npm start

> Starting production server...

> Error: EADDRINUSE: address already in use :::80

> Critical: Database schema mismatch detected.

> System crashing... [Segmentation fault]

FATAL ERROR

// THE SOLUTION

Infrastructure as Code & Automation

I implemented a fully automated pipeline to ensure consistency and reliability. Every commit is tested, built into a Docker container, and deployed with zero downtime.

CI/CD Pipeline

Leveraged GitHub Actions to run automated unit tests and build Docker images upon every push to the `main` branch.

Containerization

Packaged the application with Docker to eliminate "it works on my machine" issues and ensure environment parity.

Load Balancing

Configured Nginx as a Reverse Proxy to handle Blue-Green deployments, ensuring users experience no interruptions during updates.

Observability

Deployed Prometheus & Grafana to scrape metrics and visualize system health (CPU, Memory, Request Rate) in real-time.

Business Impact & Results

99.99% System Uptime
15m Deployment Time (Reduced from 2 hours)
0s Downtime during updates
100% Process Visibility
PROD-CLUSTER-AWS-EAST-1 STATUS: HEALTHY
THROUGHPUT (RPS)
2,450
LATENCY (P95)
32ms
ERROR RATE
0.01%
All systems operational