Change Data Capture Techniques in Legacy Data Integration

Abstract

Change data capture techniques are important in legacy data integration because older systems often contain continuously changing business records that must be synchronized with modern databases, data warehouses, and reporting platforms. Change data capture helps identify inserted, updated, and deleted records without reloading the entire legacy dataset, improving integration speed and reducing processing overhead. Existing literature highlights timestamp-based tracking, trigger-based capture, log-based capture, checksum comparison, snapshot comparison, and incremental extraction as major techniques for detecting data changes. However, many organizations still face challenges such as undocumented legacy structures, missing update timestamps, limited access to transaction logs, inconsistent delete tracking, duplicate extraction, and synchronization delays. This research is important because unreliable change detection can cause data mismatch, reporting errors, delayed migration, and weak decision support. This article discusses change data capture techniques in legacy data integration, focusing on change identification, extraction rules, source-to-target synchronization, deletion handling, staging validation, error logging, and incremental loading control. The study concludes that effective change data capture improves integration reliability, reduces data movement cost, maintains synchronization accuracy, and supports smoother modernization of legacy information systems.