I have performed data loads using full load and truncated load. During data quality checks and post‑validation checks, I analyzed and found that the data was incorrectly loaded.

Full load replaces the entire target table, while truncated load deletes all rows before inserting new data. Incorrect loads can stem from schema mismatches, missing values, or duplicate keys. To detect them, run checksums, row counts, and sample comparisons against the source. Use a staging area to validate data before moving it to production. If errors are found, rollback the transaction, correct the source data, and re‑load. Implement idempotent upserts or use a CDC pipeline to avoid full reloads. Finally, automate these checks and log results so future loads can be monitored automatically.