An In-Depth Analysis of Intelligent Data Migration Strategies from Oracle Relational Databases to Hadoop Ecosystems: Opportunities and Challenges

Abstract

The migration from Oracle relational databases to Hadoop ecosystems has become a strategic priority for many organizations seeking to benefit from big data analytics. This paper explore intelligent data migration strategies, highlighting the opportunities that arise from utilizing Hadoop's distributed storage and processing capabilities, as well as the challenges inherent in such a transition. We explore the technical and operational aspects of data migration, including data extraction, transformation, and loading (ETL), schema conversion, data quality assurance, and real-time data integration. Additionally, we examine intelligent approaches such as automation, machine learning, and AI-driven optimization to enhance the efficiency and effectiveness of the migration process. This study also addresses critical challenges, including handling large data volumes, maintaining data consistency, minimizing downtime, and ensuring compliance with data governance standards. Through a thorough analysis, we present best practices, tools, and methodologies that facilitate a smooth and efficient migration process in order to enable organizations to fully exploit the potential of Hadoop ecosystems. Finally, we propose a phased migration strategy and recommend adopting a hybrid data architecture during the transition period.