If a day's data is loaded twice, how do you detect it and fix it? Additionally, if something was wrong in yesterday's data and you need to reload a month of data, what do you do?

To detect a duplicate load, I’d first enable logging at the ingestion layer and capture a hash or checksum of each day's raw file or database snapshot. I’d store these hashes in a metadata table. If the same hash appears twice, I know a duplicate load occurred. I’d also run a record count comparison against the target warehouse; a sudden spike in row count for a specific date signals duplication. Once detected, I’d roll back the duplicate load by deleting the affected rows using a delete statement keyed on the load timestamp or a surrogate key. For the month‑long reprocessing, I’d schedule a back‑fill job that re‑extracts data for the affected period, applies the same transformation logic, and writes to a new staging area. I’d use idempotent writes (e.g., MERGE or UPSERT) to avoid double counting. After reprocessing, I’d run data quality checks to confirm counts and checksums match expected values. Finally, I’d update the metadata table to mark the reloaded period as clean and alert stakeholders that the issue is resolved.