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.
Event-driven merge propagation
Publish merge events via Kafka or message queues for decoupled consumption. Downstream systems subscribe to merge events and apply reference updates independently, enabling loose coupling.
CDC-based approaches
Change Data Capture detects merge operations at the database level and propagates them through the data pipeline. Tools like Debezium can capture merge-related row changes and forward them downstream.
Reference update strategies
When two records merge, every foreign key pointing to the “losing” record must be updated:
- Pointer swapping — update all references to point to the surviving record
- Alias tables — maintain a lookup table mapping old IDs to new IDs
- Cascade operations — database-level cascading updates
Reconciliation and audit
Downstream systems need reconciliation jobs to verify convergence after merge propagation. Audit trails track which systems have processed each merge event and flag any that haven’t.
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