SQL Guide: How to Catch Data Errors When Row Counts Look Identical

Data analyst Michael Nocito published a technical guide on August 8, 2026, explaining how to perform deep table reconciliation in SQL beyond simple row-count checks. He demonstrates that two tables can each contain 16 rows yet still differ by $400 in total revenue, illustrating how row counts alone can miss significant data errors. The method uses a full outer join on a shared key column to classify every row as matched, changed, source-only, or target-only. Nocito applied the technique to a deliberately damaged copy of a 16-row orders table loaded in DuckDB, letting the queries surface four hidden discrepancies. The guide argues that while row counts catch major loading failures, comparing column-level values is essential to validate a true migration.