 Corresponding author: Chandrasekhar Anuganti Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Data management using advanced methodologies: A comprehensive analysis of enterprise data architecture and processing frameworks Chandrasekhar Anuganti * Enterprise Infrastructure, Truist Financial Corporation, USA. World Journal of Advanced Research and Reviews, 2024, 21(01), 2983-2992 Publication history: Received on 06 December 2023; revised on 19 January 2024; accepted on 27 January 2024 Article DOI: https://doi.org/10.30574/wjarr.2024.21.1.0182 Abstract The evolution of enterprise data management has necessitated the adoption of advanced methodologies to handle the increasing volume, velocity, and variety of data in modern financial institutions. This article presents a comprehensive analysis of data management practices implemented in large-scale enterprise environments, focusing on the integration of traditional data warehousing with contemporary big data technologies. Through examination of real-world implementations at major financial institutions, this study explores the architectural patterns, methodologies, and best practices that enable effective data governance, processing, and analytics. The research demonstrates how organizations can successfully integrate heterogeneous data sources while maintaining data quality, regulatory compliance, and operational efficiency through advanced ETL frameworks, automated validation processes, and hybrid cloud-on-premises architectures. Keywords: Enterprise Data Management; Extract Transform And Load (ETL); Hadoop Distributed File System; ETL Processing Framework; Data Validation 1. Introduction In the contemporary data-driven landscape, financial institutions face unprecedented challenges in managing vast volumes of structured and unstructured data while adhering to stringent regulatory requirements. The complexity of modern data ecosystems, encompassing traditional relational databases, enterprise resource planning systems, big data platforms, and cloud-based solutions, demands sophisticated methodologies for effective data management. This article examines advanced data management practices implemented in enterprise environments, with particular emphasis on the integration of Extract, Transform, and Load (ETL) processes with big data technologies and automated governance frameworks. The research presented herein draws from extensive experience in enterprise data management implementations, specifically focusing on methodologies employed in large-scale financial institutions handling federal regulatory reporting, credit risk analysis, commercial banking operations, and mortgage lending processes. The analysis encompasses architectural design patterns, implementation strategies, and operational best practices that ensure data integrity, accessibility, and compliance with regulatory standards.