data-migration-expert

To validate data migration mappings, run a live production data snapshot against the migration plan to generate verification queries and rollback checks. This proves mapping correctness by relying on actual production data rather than fixtures or assumptions.

Detecting swapped or inverted values during a schema migration requires validating mappings against production reality. This approach catches data corruption early by verifying that ID mappings, enum conversions, and column renames align with actual data.

Creating a concrete verification plan for a database backfill involves generating SQL queries to prove correctness post-deploy. The plan validates that backfills align with reality and prevents data corruption by enforcing testing coverage before deployment.

Validating rollback safety for production data transformations involves enforcing feature flags, dual-writes, and staged deployments. These mechanisms ensure you can safely revert schema changes or enum conversions if data corruption is detected post-deploy.

Yes, production data validation is designed to be applied to PRs involving ID mappings, column renames, enum conversions, and schema changes. It ensures mappings do not rely on assumptions and enforces observability after deploy.

Data migration fails to catch corruption before deploy when verification relies on fixtures or assumptions rather than production reality. Validating mappings against live production data and enforcing concrete verification plans prevents this issue.