My Cloud Resume Challenge Journey

My Cloud Resume Challenge Journey — A Deep Technical Learning Experience

After several intense weeks, my Cloud Resume Challenge project is **LIVE**! This was a deep dive into full-stack cloud architecture, that pushed me far beyond theory, requiring real architecture, real automation, real problem-solving, and real deployment.

Check out the live site and source code.

🔗 **Live Project:** https://www.michailkakos-resume.com
💻 **Source Code:** https://lnkd.in/eHgdf8xD

What is the Cloud Resume Challenge?

The Cloud Resume Challenge (CRC) is a practical, end-to-end cloud project that requires you to:

Build and host a resume website in the cloud
Implement a serverless backend with a visitor counter
Store data in a managed NoSQL database
Use Infrastructure as Code
Deploy via automated CI/CD pipelines
Prove your cloud, programming, security, and DevOps skills in one integrated solution

It simulates the real workflows of cloud engineers and solutions architects — not just passing an exam but building something that works at production-level standards.

Why I Took Up This Challenge

Coming from a network engineering background, I wanted a project that would push me beyond networking into full cloud architecture, but I had to grow in:

Python backend development
Serverless event-driven patterns
NoSQL modeling(DynamoDB)
Terraform IaC structures
CI/CD automation and testing
Cloud-native observability

This project truly bridges traditional networking with modern cloud engineering.

Architecture of the Solution

Frontend

S3 – static hosting
CloudFront – global CDN with caching policies
ACM + Route 53 – HTTPS + custom DNS routing

Backend

API Gateway (REST) – visitor request routing
AWS Lambda (Python) – business logic, geo-lookup, idempotent updates
DynamoDB – visitor counter + geolocation analytics

DevOps & Automation

Terraform – fully modular Infrastructure as Code
GitHub Actions – CI/CD with: Pytest unit + integration testing

What Were the Biggest Challenges?

Terraform state & modularisation: Building reusable, hierarchical modules while managing state safely.
DynamoDB conditional updates: Ensuring atomic increments during concurrent requests.
CI/CD packaging for Lambda: Correctly bundling dependencies and tests for Python Lambda deployments.
CloudFront invalidation automation: Ensuring new versions deploy globally without downtime.
Data model design: Supporting visitor counting + geolocation analytics with a single-table pattern.
Security hardening: IAM least privilege, bot protection, API throttling, and misconfiguration prevention.

Key Takeaways

Cloud engineering requires end-to-end thinking, not just service knowledge
CI/CD transforms development velocity and consistency
Serverless simplifies ops but requires good design and testing
IaC is essential for modern infrastructure reproducibility
Logging, monitoring, and metrics matter from day one
The best learning happens by building something real

Final Thoughts

This challenge wasn’t just a project — it was a hands-on simulation of real cloud engineering work:

Architect. Develop. Automate. Secure. Monitor. Iterate.

It helped me push beyond tutorials to design a system that:

Scales globally
Deploys automatically
Operates securely
Produces real analytics
Demonstrates practical, job-ready cloud engineering skills

I highly recommend it for anyone seeking real-world experience.