Google Cloud & DevOps
GCP projects/IAM, Cloud Storage & DNS, Compute Engine, Cloud Run, Cloud SQL, and DevOps practices
Phase Goal
Become cloud-portable: apply the mental models from AWS to Google Cloud, deploy containers serverlessly on Cloud Run, and adopt the DevOps practices (monitoring, secrets, IaC) that keep production healthy.
Open the written lectures for this course before checking off the phase topics.
Day 157: GCP Mental Model & Setup
Day 158: Cloud Storage & Cloud DNS
Day 159: Compute Engine & Networking
Day 160: Cloud Run — Deploy Containers Serverlessly
Day 161: Cloud SQL & DevOps Practices
Day 162: Project — Water-Quality Telemetry Reliability on GCP
Water-Quality Telemetry Reliability on GCP — DEPLOYED
Deploy a telemetry intake and public-status service to Cloud Run with Cloud SQL and Cloud Storage dependencies, Secret Manager, a custom domain, and CI/CD through workload identity. Define freshness and availability objectives, test stale-sensor and database failures, wire actionable alerts, record costs, and prove rollback.
- Cloud Run service with Secret Manager, Cloud SQL/Storage integration, and a custom HTTPS domain.
- GitHub Actions deployment through workload identity—no long-lived cloud keys.
- A dashboard or README scorecard covering health, latency, errors, alerts, cost, and rollback.
Phase Complete!
After this phase, you'll be able to:
- GCP hierarchy, IAM/service accounts, gcloud CLI
- Cloud Storage (signed URLs) and Cloud DNS
- Compute Engine VMs, VPC, firewall rules, load balancing
- Cloud Run: container deploys, scaling, secrets, custom domains
- Cloud SQL, Secret Manager, and Cloud Logging/Monitoring
- DevOps: observability, alerting, IaC (Terraform), CI/CD to Cloud Run
You are cloud-portable and DevOps-literate: you can deploy and operate containerized apps on a second major cloud with monitoring and IaC — exactly what cloud/DevOps interviews probe.