Every System.
Always in Sync.

What Changes

What it looks like when data sync is actually real-time.

Most integration architectures are batch-based: systems export data on a schedule, files are transferred, and the destination system imports them. This works adequately for historical reporting or low-frequency data. It breaks down wherever decisions depend on current information — inventory, customer records, pricing, and order status. Real-time synchronization replaces the batch pattern with event-driven propagation. Rather than asking "has anything changed since the last sync?" on a schedule, the source system publishes a change event the moment a record is updated. Connected systems subscribe to those events and update themselves immediately.

The right synchronization architecture depends on the systems involved and the volume of changes. For database-to-database scenarios, Change Data Capture (CDC) reads the transaction log directly — capturing every insert, update, and delete with no impact on source system performance. For cloud-to-cloud integrations, webhook APIs provide near-instant push notifications when records change. For high-volume scenarios or fan-out to multiple consumers, message queues provide durability and guaranteed delivery. Bidirectional sync requires additional care: conflict resolution logic must determine what happens when both systems update the same record before sync runs.

Not every integration needs real-time sync — and over-engineering adds unnecessary complexity. Batch sync is appropriate for use cases where latency isn't operationally significant: nightly financial consolidations, weekly HR data exports, monthly customer list updates for marketing. Real-time is necessary when the lag between a change and its propagation causes operational errors, customer-facing inaccuracies, or downstream process failures.