Optimizing ETL Processes for High-Volume Data Warehousing in Financial Applications

Abstract

The Extract, Transform, Load (ETL) process is a critical backbone in financial data warehousing, where large-scale data volumes demand optimized performance to meet industry requirements. Financial institutions rely heavily on ETL systems to integrate, cleanse, and structure data for decision-making and regulatory compliance. This paper delves into the optimization of ETL processes for high-volume data warehousing in financial applications. By analyzing current challenges, exploring advanced architectures, and incorporating emerging technologies such as Big Data frameworks and cloud solutions, we present a comprehensive framework for enhanced ETL efficiency. The study also evaluates performance metrics and addresses critical concerns like data security and compliance, paving the way for scalable and resilient financial data warehousing systems.