Innovative Data Migration Methods for Large-Scale Systems

Abstract

This paper explores innovative data migration methods aimed at addressing the complexities and challenges of migrating large-scale systems. Recognizing the critical importance of data migration for system upgrades, consolidations, and cloud transitions, the study delves into the specific hurdles faced by large-scale organizations, such as data volume, integrity, security, and system performance. Traditional approaches like offline, online, and hybrid migrations are scrutinized for their advantages and drawbacks. The research identifies modern solutions, including cloud-based migration, real-time data replication, and the use of automated tools and AI, to enhance efficiency, accuracy, and security. Through empirical evaluation, the study demonstrates how these innovative methods can effectively minimize downtime, maintain data consistency, and optimize resource use, providing valuable insights and recommendations for organizations undertaking complex data migration projects.