Change Data Capture for High- Volume Transaction Systems with Consistency Validation

Abstract

High-volume transaction systems require CDC pipelines that can move committed database changes without weakening transaction order, event identity, or source-target consistency. This article presents a consistency-aware CDC framework that reads changes from transaction logs, preserves log sequence ordering, assigns deterministic event identifiers, aligns snapshot initialization with incremental replay, and validates target state through reconciliation windows. Simulated results show that the full framework reduced CDC latency from 820 ms to 145 ms compared with table polling. Event ordering accuracy increased from 84.6% to 99.2%, while duplicate suppression improved from 71.5% to 99.5%. Under transaction loads from 25k to 500k transactions per minute, consistency validation success reached 98.9%, snapshot alignment accuracy reached 99.1%, and throughput stability reached 96.8%. The results show that reliable CDC depends on coordinated log reading, commit-order preservation, replay-safe delivery, and continuous validation rather than capture speed alone.