Introduction
Theory builds knowledge, but projects build expertise. In the world of multi-cloud engineering, nothing teaches better than deploying a real-world workload across two different providers.
In this lab, we’ll build and deploy a simple web application split between AWS and Google Cloud Platform (GCP):
- Frontend: Hosted on AWS (using S3 + CloudFront).
- Backend API: Deployed on GCP (Cloud Run or GKE).
- Database: Optionally in AWS RDS (PostgreSQL).
- Networking: Cross-cloud communication secured with HTTPS.
At CuriosityTech.in, learners complete similar projects in guided workshops, where the goal is not just to deploy—but to understand why each choice is made.
Step 1 – Architecture Blueprint
Diagram (explained in words): This hybrid deployment illustrates cross-cloud dependency management.
Step 2 – Prerequisites
- AWS Account + CLI configured.
- GCP Account + gcloud CLI configured.
- Basic knowledge of containerization (Docker).
- A sample web app (React frontend + Node.js backend).
Step 3 – Frontend Deployment on AWS
- Create an S3 bucket → Enable static website hosting.
- Build the React app →
npm run build. - Upload files to S3 →
aws s3 sync build/ s3://myfrontend-bucket/. - Configure CloudFront → Point to S3 as origin, enable HTTPS.
- DNS setup → Use Route 53 to map domain → CloudFront.
Checkpoint: Visiting your domain should load the static frontend.
Step 4 – Backend Deployment on GCP
- Containerize the Node.js backend
- Create Dockerfile.
- Build & push to Google Container Registry (GCR):
gcloud builds submit --tag gcr.io/PROJECT_ID/backend-app
- Deploy to Cloud Run (serverless container hosting):
gcloud run deploy backend-app \ --image gcr.io/PROJECT_ID/backend-app \ --platform managed \ --allow-unauthenticated - Note the generated HTTPS endpoint → e.g.,
https://backend-app-xyz.a.run.app.
Checkpoint: Run curl <endpoint> to verify backend works.
Step 5 – Database on AWS
- Create RDS instance (PostgreSQL).
- Enable security group access for GCP backend IP ranges.
- Store DB credentials in AWS Secrets Manager (optional).
- Update backend app to connect to RDS endpoint.
Checkpoint: Backend should return live data from RDS.
Step 6 – Connect Frontend & Backend
- In React frontend → update API base URL to GCP backend endpoint.
- Rebuild & re-upload to S3.
- Test full flow → Frontend (AWS) → API (GCP) → Database (AWS).
Step 7 – Monitoring & Logging
- AWS CloudWatch → Monitor S3/CloudFront.
- GCP Cloud Monitoring → Monitor backend API.
- Export logs into a centralized Grafana dashboard.
At CuriosityTech.in labs, we show how to use OpenTelemetry to collect unified metrics across both clouds.
Step 8 – Cost Optimization Considerations
- Use AWS S3 + CloudFront for cost-efficient global content delivery.
- Cloud Run charges only per-request, saving idle costs.
- RDS reserved instances reduce DB spend.
- Monitor data egress costs between AWS and GCP—often overlooked.
Step 9 – Security Controls
- HTTPS enforced for all communication.
- IAM roles: principle of least privilege across both clouds.
- Store secrets in AWS Secrets Manager or GCP Secret Manager.
- Enable logging for all API calls (CloudTrail + GCP Audit Logs).
Step 10 – Real-World Pitfalls
- Latency: Cross-cloud communication can add milliseconds → consider caching.
- IAM Confusion: Mixing AWS IAM + GCP IAM roles leads to misconfigurations.
- Debugging: Errors may occur in one cloud but surface in another.
- Billing Surprises: Inter-cloud data transfer adds hidden costs.
Lab Extension (For Advanced Learners)
- Deploy backend on GKE (Google Kubernetes Engine) instead of Cloud Run.
- Use Terraform to define infrastructure in both clouds.
- Add CI/CD pipelines with GitHub Actions → AWS + GCP deploys.
- Experiment with multi-cloud failover (if GCP API fails → switch to AWS Lambda).
Conclusion
This project shows how a single application can span AWS and GCP, demonstrating the realities of multi-cloud engineering: balancing performance, security, and cost while managing complexity.
At CuriosityTech.in, we emphasize hands-on labs because reading about multi-cloud isn’t enough. Engineers must deploy, break, fix, and optimize in real projects to gain mastery. Completing this lab is the first step toward becoming a multi-cloud architect capable of bridging platforms seamlessly.
