Data management using advanced methodologies: A comprehensive analysis of enterprise data architecture and processing frameworks
Enterprise Infrastructure, Truist Financial Corporation, USA.
Research Article
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
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
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0