GCP
QA Scenarios
Each scenario shows how multiple GCP services chain together to deliver a real-world QA outcome — from nightly regression to chaos engineering. Use them as blueprints.
CI/CD with Automation + Performance Gates
Cloud Build → Artifact Registry → Cloud Deploy promotes through dev/qa/prod; verify jobs run Playwright and k6 as quality gates.
Serverless Nightly Regression with Workflows
Cloud Scheduler kicks a Workflow that seeds data, runs API + UI automation in parallel Cloud Run Jobs, aggregates to BigQuery.
Massively Parallel Playwright on Cloud Run Jobs
Use Cloud Run Jobs task parallelism to run hundreds of Playwright shards concurrently, then aggregate via a Workflow.
Distributed k6 Load Testing on GKE
k6-operator runs distributed load tests on GKE; results stream to BigQuery; Cloud Trace pinpoints latency hotspots.
Mobile Automation Matrix with Firebase Test Lab
Per-PR Android matrix tests on real devices; results streamed to BigQuery; merge gated in Cloud Build.
Kubernetes Test Runner on GKE with Results in Cloud SQL
GKE Autopilot runs Playwright/k6 Jobs scaled by KEDA from Pub/Sub; raw artifacts go to GCS, structured results stream into Cloud SQL (PostgreSQL) for BigQuery-backed dashboards.