Enterprise App Migration to Google Cloud (GCP)

This repository provides a production-style reference implementation for migrating a Python/Flask "Enterprise App" from an AWS-based architecture to modernized deployments on Google Cloud Platform (GCP).

The implementation demonstrates:

• An AWS baseline architecture representative of real-world workloads
• A re‑platformed design on GCP Compute Engine (Managed Instance Group)
• A serverless container deployment on Cloud Run with integrated CI/CD

All detailed design, runbooks, and operational procedures are documented under the docs/ folder and in the platform-specific deployment guides.

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Quick Overview

The same Flask application is deployed in three ways:

1. AWS Baseline – EC2 + Application Load Balancer + DynamoDB + S3
2. GCP Option 1 – Compute Engine Managed Instance Group + HTTPS Load Balancer + Firestore + Cloud Storage
3. GCP Option 2 – Cloud Run (serverless containers) + Artifact Registry + CI/CD with GitHub Actions

Each option is fully described in architecture and deployment docs under docs/ and in the deployment_*.md files in each platform folder.

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High-Level Architecture (At a Glance)

• Web tier: Flask app served via EC2, Compute Engine, or Cloud Run
• Data tier: DynamoDB (AWS) or Firestore (GCP)
• Object storage: S3 (AWS) or Cloud Storage (GCP)
• Identity: IAM roles (AWS) or service accounts with IAM (GCP)

Detailed diagrams and flows are in docs/architecture.md.

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Technology Stack (Summary)

• Language & Framework: Python, Flask, Jinja2
• AWS: EC2, Auto Scaling, ALB, DynamoDB, S3, IAM
• GCP (Compute Engine): Compute Engine, MIG, HTTPS Load Balancer, Firestore (Datastore mode), Cloud Storage, VPC, Cloud NAT, service accounts
• GCP (Cloud Run): Cloud Run, Artifact Registry, Firestore, Cloud Storage, Secret Manager
• Automation & Tooling: Terraform, Google Cloud SDK, AWS CLI, Docker, GitHub Actions

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How to Use This Repository

1. Understand the architecture
   Read the full architecture and migration story:

   • docs/architecture.md

2. Understand the deployment model
   Review common deployment principles (Terraform, environments, CI/CD):

   • docs/deployment.md

3. Pick a deployment path and follow its guide

   • AWS baseline (EC2): AWS/deployment_aws.md
   • GCP Compute Engine (Option 1): GCP/deployment_gce.md
   • GCP Cloud Run (Option 2): GCP-Cloud-Run/deployment_cloud_run.md

4. Operate and optimize

   • Troubleshooting tips and common errors:
     • docs/troubleshooting.md
   • Cost guidance and efficiency tuning:
     • docs/cost-optimization.md

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Repository Layout (Short)

/
├── AWS/                # AWS baseline app + Terraform + deployment_aws.md
├── GCP/                # GCE (Option 1) app + Terraform + deployment_gce.md
├── GCP-Cloud-Run/      # Cloud Run (Option 2) Terraform + deployment_cloud_run.md
├── docs/               # Architecture, deployment model, troubleshooting, cost
└── .github/workflows/  # cloud-run-deploy.yml (Cloud Run CI/CD)

For all implementation details and diagrams, start in:

• docs/architecture.md
• docs/deployment.md