Data Consistency Checking Between Source and Target Databases

Abstract

Data consistency checking between source and target databases is important because integrated, migrated, or replicated data must remain accurate and reliable across different systems. Consistency checking helps verify whether records, values, keys, relationships, and business rules are correctly transferred from the source database to the target database. Existing literature highlights source-to-target comparison, record count validation, checksum matching, referential integrity checks, duplicate detection, field-level comparison, and reconciliation reports as major methods for validating database consistency. However, many organizations still face challenges such as missing records, datatype mismatches, transformation errors, duplicate loads, delayed synchronization, and inconsistent business rule application. This research is important because inconsistent source and target data can affect reporting accuracy, transaction reliability, compliance monitoring, and enterprise decision-making. This article discusses data consistency checking between source and target databases, focusing on validation rules, key matching, value comparison, checksum verification, exception reporting, and audit-based reconciliation. The study concludes that effective consistency checking improves data accuracy, reduces integration risk, strengthens migration reliability, and supports trusted enterprise information management.