ETL Performance Optimization in Relational Data Warehousing

Abstract

Enterprise decision support systems require consistent, integrated, and high-quality data to support managerial planning, performance monitoring, and long-term strategic decisions. Data warehouse design provides the structural foundation for collecting data from multiple operational systems and converting it into reliable analytical information. Existing literature highlights dimensional modeling, ETL processing, OLAP analysis, metadata management, and data quality control as major components of enterprise data warehousing. However, many organizations still face problems such as fragmented data sources, slow reporting cycles, duplicate records, weak data governance, and inconsistent decision outputs across departments. This research is important because enterprises need scalable and traceable data environments that can support accurate, timely, and evidence-based decision-making. This article discusses the design of a data warehouse for enterprise decision support systems, focusing on data source integration, schema design, fact and dimension modeling, ETL workflow, data validation, query optimization, and reporting architecture. The study concludes that a well-designed data warehouse improves analytical accuracy, reduces data redundancy, strengthens cross-functional visibility, and supports better enterprise-level decision-making.