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An enterprise is planning to migrate a three-tier web application from an on-premises data center to Google Cloud. Based on workload assessments, the architecture team has chosen a mixed migration approach:
You need to establish a testing and validation protocol to verify the application's functional integrity, scalability, and performance before executing the final production cutover.
Which validation strategy should you implement?
Capture source performance baselines, execute functional parity tests with identical input/output scenarios across all tiers, perform incremental load testing on Cloud Run and Cloud SQL, and conduct an end-to-end migration dry run.
Execute the production cutover first, monitor the four golden signals using Google Cloud Observability in production, and dynamically rightsize Cloud SQL and Compute Engine based on live error rates.
Refactor the unit test suites to mock all database dependencies, perform synthetic browser UI tests against Cloud Run endpoints, and execute a single big-bang cutover during a scheduled maintenance window.
Conduct static code security analysis on container images in Artifact Registry, enable binary authorization, and perform read-only shadow queries against the target database during non-business hours.
Capture source performance baselines, execute functional parity tests with identical input/output scenarios across all tiers, perform incremental load testing on Cloud Run and Cloud SQL, and conduct an end-to-end migration dry run.
A structured validation and testing protocol is an essential phase in cloud migration that systematically assesses migrated workloads across multiple tiers and strategies. It ensures that workloads rehosted to Compute Engine, replatformed to Cloud SQL, and refactored onto Cloud Run satisfy functional specifications, performance Service Level Objectives (SLOs), and operational readiness criteria prior to cutting over production traffic.
Combining baseline benchmarking, end-to-end functional testing, staged load testing, and migration dry runs directly addresses the challenges associated with combining rehosting, replatforming, and refactoring strategies. It ensures that every tier is rigorously verified for stability and performance.
Execute the production cutover first, monitor the four golden signals using Google Cloud Observability in production, and dynamically rightsize Cloud SQL and Compute Engine based on live error rates.
Refactor the unit test suites to mock all database dependencies, perform synthetic browser UI tests against Cloud Run endpoints, and execute a single big-bang cutover during a scheduled maintenance window.
Conduct static code security analysis on container images in Artifact Registry, enable binary authorization, and perform read-only shadow queries against the target database during non-business hours.
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