Relational Schema Mapping for Legacy System Integration

Abstract

Relational schema mapping is important for legacy system integration because older applications often store business data in formats that do not directly match modern relational database structures. Schema mapping helps convert legacy files, tables, fields, codes, and relationships into structured relational models that support integration, reporting, and long-term system modernization. Existing literature highlights entity mapping, attribute matching, data type conversion, key identification, relationship mapping, normalization, and transformation rules as major practices in legacy database integration. However, many organizations still face challenges such as undocumented legacy schemas, inconsistent field definitions, missing primary keys, duplicated records, incompatible data types, and weak referential integrity. This research is important because inaccurate schema mapping can lead to data loss, integration errors, reporting inconsistencies, and failure of modernization projects. This article discusses relational schema mapping for legacy system integration, focusing on source schema analysis, target schema design, entity-relationship mapping, data transformation, constraint definition, validation rules, and migration testing. The study concludes that effective schema mapping improves data consistency, reduces integration risk, supports reliable migration, and strengthens enterprise database modernization.