The first version of a data reconciliation system almost always has the same failure mode: it works correctly, then gets deployed to production, and immediately generates hundreds of duplicate alerts. The same discrepancy is reported every time the job runs. Operators learn to ignore the alert channel within a week. The system that was supposed to improve data reliability becomes noise. The root cause is the same in almost every case: the comparison engine was built as a stateless script. Each run produces a fresh list of discrepancies with no awareness of what previous runs found. Without state management, every run is the first run. Making a reconciliation system idempotent - ensuring that running it multiple times produces the same observable outcome as running it once - requires both deterministic comparison logic and persistent state that survives between runs. This piece covers the patterns that make that work in production.…