Implementing AI-Driven Backup and Recovery Strategies in Modern Database Systems

Abstract

The increasing complexity and volume of data in modern database systems demand advanced methods for backup and recovery. Traditional strategies often fall short in addressing the scale and dynamism of contemporary data environments. This paper explores the implementation of AI-driven backup and recovery strategies, focusing on their ability to enhance efficiency, reliability, and scalability. By leveraging machine learning algorithms and predictive analytics, AI-driven approaches offer improved fault tolerance and faster recovery times compared to conventional methods. This research provides a comprehensive review of current AI techniques in backup and recovery, presents case studies of successful implementations, and discusses future directions in this field.