Change Data Capture (CDC)
Real-Time Data Synchronization Explained
Bane Tech Engineering · 6 min read
Real-time data synchronization keeps data consistent across multiple systems as it changes, rather than in nightly or hourly batches. When a record is updated in one system, the change reaches every other system that depends on it within seconds or less.
Why batch is no longer enough
Batch jobs leave systems out of step for hours. Customers see outdated balances, stock levels drift between channels and analytics trail operations. As processes become digital and continuous, the window of inconsistency becomes a business risk.
Common techniques
- Change data capture — stream committed database changes. See What Is Change Data Capture (CDC)?.
- Application events — services publish domain events. See Event-Driven Integration Architecture.
- API calls and webhooks — push changes directly to dependent systems.
Consistency and conflicts
Most distributed synchronization is eventually consistent: targets converge shortly after the source. Designs should define a system of record for each entity, preserve ordering per key and make writes idempotent.
Bidirectional synchronization adds conflict resolution — for example last-writer-wins, field-level ownership or explicit merge rules — and loop prevention so a change does not echo back to its origin.
Making synchronization reliable
- Buffer changes so target outages do not lose data.
- Track lag and failed records per target.
- Support re-synchronization from a snapshot.
- Version schemas and validate payloads.
- Reconcile periodically to detect drift.