A Comprehensive Analysis of Intelligent Data Migration Strategies: Exploring the Transition from Legacy Relational Databases to Hadoop Ecosystems in Autonomous Vehicle Data Systems

Abstract

The evolution of autonomous vehicle (AV) systems has led to an exponential increase in data generation, necessitating robust and scalable data management solutions. Traditional relational databases, while reliable, are often inadequate in handling the volume, variety, and velocity of data generated by AV systems. The shift towards Hadoop ecosystems offers a promising alternative, leveraging distributed storage and parallel processing to accommodate big data requirements. This paper presents a comprehensive analysis of intelligent data migration strategies for transitioning from legacy relational databases to Hadoop ecosystems within AV data systems. It explores the unique challenges posed by AV data, including real-time processing needs, data heterogeneity, and security considerations. The discussion extends to the architectural differences between relational databases and Hadoop ecosystems, emphasizing the suitability of Hadoop for unstructured and semi-structured data. Furthermore, this paper outlines the strategic steps involved in migration, from data assessment and extraction to transformation and loading (ETL), while ensuring data integrity and minimal disruption to ongoing operations. The potential of machine learning and automation in optimizing migration processes is also examined, highlighting how these technologies can enhance efficiency and reduce human error. Finally, the paper considers post-migration considerations, such as data governance, compliance with regulatory standards, and performance optimization, providing a holistic view of the data migration landscape in the context of autonomous vehicle systems.