Data Migration from Legacy Systems to Relational Databases

Abstract

Data migration from legacy systems to relational databases is an important process for modernizing enterprise information systems and improving long-term data accessibility. Legacy systems often store business data in outdated formats, isolated applications, flat files, or older database structures that limit integration, reporting, and system scalability. Existing literature highlights data extraction, data mapping, transformation, validation, cleansing, schema conversion, and migration testing as major activities in legacy system migration. However, many organizations still face challenges such as incompatible data formats, incomplete documentation, duplicate records, missing values, weak referential integrity, and business disruption during migration. This research is important because enterprises need reliable migration strategies that preserve data accuracy while enabling improved performance, integration, and decision support in relational database environments. This article discusses data migration from legacy systems to relational databases, focusing on source system analysis, schema redesign, data cleansing, transformation rules, loading procedures, validation checks, and post-migration verification. The study concludes that a structured migration approach improves data consistency, reduces operational risk, supports system modernization, and strengthens enterprise-level data management.