Idempotent Data Reconciliation - Production Patterns That Don't Create Noise

A data reconciliation system fails in production due to duplicate alerts from a stateless comparison engine. To fix this, make the system idempotent by adding deterministic comparison logic and persistent state. This requires a stable identifier for each discrepancy, such as a hash of the record's comparison key, field name, and values. Implementing this pattern ensures that running the system multiple times produces the same outcome as running it once. Engineers should use this pattern to prevent noise in their data reconciliation systems.