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Insights on data reconciliation, migrations, and proving your data is right
Why row-count checks miss data loss in a database migration
A source and target with matching row counts can still have lost or corrupted data. Here is where the losses hide — nulls, type and precision drift, shifted joins, and encoding — with Oracle-to-PostgreSQL examples.
Read article →Deterministic vs statistical data quality: when you need proof, not probability
Anomaly detection tells you a table looks unusual. Reconciliation proves exactly which values differ. When each approach is the right tool, and why the difference matters for migrations, finance, and regulated reporting.
Read article →How to validate a database migration cutover with reconciliation
A practical checklist for proving parity before you switch systems off — from choosing a comparison key to wiring reconciliation into a CI/CD cutover gate with the CLI.
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