From Merge Decision to System-Wide Consistency
Matching and merging records is only half the battle. The real challenge is propagating those merge decisions across every downstream system — CRMs, billing platforms, analytics warehouses — without creating data inconsistencies or breaking references. This article covers the architectural patterns that make merge propagation reliable at scale.
Key Topics Covered
- Event-driven merge propagation — publishing merge events via Kafka or message queues for decoupled consumption.
- CDC-based approaches — using Change Data Capture to detect and propagate merge operations.
- Reference update strategies — pointer swapping, alias tables, and cascade deletion.
- Reconciliation and audit patterns — ensuring downstream systems converge to the merged state.
- Handling merge conflicts and rollback scenarios in distributed systems.