Building Scalable Data Warehouses for Financial Analytics in Large Enterprises

Authors

Naveen Edapurath Vijayan

Abstract

In today's digital era, large enterprises face the daunting task of managing and analyzing vast volumes of financial data to inform strategic decision-making and maintain a competitive edge. Traditional data warehousing solutions often fall short in addressing the scale, complexity, and performance demands of modern financial analytics. This paper explores the architectural principles, technological strategies, and best practices essential for building scalable data warehouses tailored to the needs of financial analytics in large organizations. It delves into data integration techniques, performance optimization methods, security measures, and compliance with regulatory standards. Through in-depth analysis and real-world case studies, the paper provides a comprehensive roadmap for practitioners aiming to design and implement robust, scalable, and secure data warehousing solutions.

Keywords

Scalable Data Warehouses, Financial Analytics, Large Enterprises, Data Integration, ETL Processes, ELT Processes, Data Modeling, Data Vault Modeling, Dimensional Modeling, Performance Optimization, In-Memory Computing, Columnar Storage, Data Security, Data Governance, Regulatory Compliance, Cloud-Based Solutions, Hybrid Architectures, Data Quality Management, Big Data Analytics, Data Warehouse Automation.

Building Scalable Data Warehouses for Financial Analytics in Large Enterprises. Naveen Edapurath Vijayan. 2024. IJIRCT, Volume 10, Issue 3. Pages 1-10. https://www.ijirct.org/viewPaper.php?paperId=2412050