Database & platform migration validation
Catch the silent data loss that row-count checks miss — and prove parity before you cut over.
Row counts match. The data doesn't.
Migrating between database engines — Oracle to PostgreSQL, say — or between platforms is where silent data loss hides. The row counts line up, so it looks fine. But values drift, joins shift, and nulls get mangled in the translation between type systems and dialects.
A schema check and a row-count check won't catch any of that. By the time someone notices the values are wrong, the old system may already be gone.
Prove parity, cell by cell, before you switch over
Value-level comparison across different engines — the checks a schema diff can't do.
Compare across different engines
Value-level comparison between different database engines — Postgres, Oracle, DB2, SQL Server, MySQL.
Catch what counts miss
Surface the drifted values, mangled nulls, and shifted joins that schema and row-count checks skip.
A repeatable cutover gate
Run the same reconciliation as a gate before every cutover attempt until parity is clean.
Drill down to exact mismatches
See precisely which rows and columns differ so you can fix the migration, not guess.
Prove parity before you switch
Cut over with confidence — with reproducible evidence the target matches the source.
Runs in your pipeline
Drive validation from the DataRecs CLI or Terraform provider so it runs inside CI/CD.
Wire it into CI/CD
Migration validation shouldn't be a one-off manual step. Use the DataRecs CLI and Terraform / OpenTofu provider to define reconciliation as code and run it as an automated gate in your migration pipeline.
- DataRecs CLI for scripted, repeatable runs
- Terraform / OpenTofu provider for infrastructure-as-code
- Fail the pipeline when parity isn't met
- Version-controlled, reproducible validation
Don't cut over on a hunch
Run a value-level reconciliation between your old and new systems and see exactly where they differ — before you switch off the source.