DATA LAKES AND WAREHOUSES IN BANKING: SECURITY FRAMEWORKS AND IMPLEMENTATION STRATEGIES

Abstract

Financial institutions face unprecedented challenges in securing and managing vast amounts of sensitive data while maintaining operational efficiency and regulatory compliance. This comprehensive article examines the intricate landscape of secure data lakes and warehouses in cloud environments, specifically tailored for financial data storage and analytics. This article explores the fundamental differences between data lakes and warehouses, analyzing their respective roles in handling structured and unstructured financial data. This article presents a detailed framework for implementing robust security measures, including encryption protocols, access control mechanisms, and monitoring systems, while addressing the unique challenges of cloud deployment. Through analysis of real-world applications in banking and financial services, this article demonstrates how these secure data architectures enable critical functions such as fraud detection, customer analytics, and real-time decision-making. Special attention is given to compliance requirements, data masking techniques, and role-based access controls that are essential for protecting sensitive financial information. This article suggests that a well-architected combination of data lakes and warehouses, supported by comprehensive security controls, provides financial institutions with the flexibility and protection needed in today's dynamic digital landscape. This article serves as a practical guide for financial technology professionals, security architects, and compliance officers seeking to implement or enhance their data storage infrastructure while maintaining the highest security standards.