Enterprise Data Lakes for Financial Services

Authors

Satyam Chauhan

Abstract

Enterprise Data Lakes (EDLs) have emerged as transformative solutions for financial institutions seeking to address challenges associated with fragmented data systems, inefficient workflows, and stringent regulatory compliance. This paper presents an AWS-centric framework for building resilient and scalable EDLs tailored to the needs of financial services. Key aspects such as controlled change management, robust failover protocols, workflow orchestration, and secure data sharing are analyzed in detail. The study explores the design and implementation of data pipelines, leveraging AWS tools like Glue, S3, Sage Maker, and CloudWatch to ensure scalability, security, and operational efficiency. A case study of an investment bank demonstrates tangible benefits, including improved workflow uptime, reduced manual interventions, and accelerated regulatory compliance. The paper concludes with insights into future directions, including AI-based anomaly detection, quantum computing, and multi-cloud architectures, highlighting the potential for financial institutions to enhance data-driven decision-making through innovative EDL strategies.

Keywords

AWS,Anomaly Detection, Change Management, Compliance, Data Pipeline Design, Enterprise Data Lake, Financial Services, Failover Protocols, Multi-Cloud Architectures, Quantum Computing, Secure Data Sharing, Workflow Orchestration.

Enterprise Data Lakes for Financial Services. Satyam Chauhan. 2019. IJIRCT, Volume 5, Issue 5. Pages 1-17. https://www.ijirct.org/viewPaper.php?paperId=2412054