Approaches to Disaster Recovery in Cloud Databases: A Comparative Analysis of Current Mechanisms

Abstract

Disaster recovery (DR) is a critical aspect of maintaining the availability, integrity, and continuity of cloud databases, which store and manage vast amounts of mission-critical data. With the increasing reliance on cloud platforms for business operations, organizations must implement effective disaster recovery mechanisms to safeguard against data loss, downtime, and system failures. This paper presents a comparative analysis of current disaster recovery approaches in cloud databases, examining the strengths and weaknesses of various strategies, including backup and restore, data replication, and automated failover systems. We evaluate how these mechanisms are applied in different cloud models (public, private, and hybrid), considering factors such as recovery time objectives (RTO), recovery point objectives (RPO), cost, and scalability. The paper also discusses emerging technologies and trends in cloud DR, such as real-time data synchronization, machine learning-based predictive recovery, and multi-cloud disaster recovery solutions. Additionally, the role of cloud service providers’ Service Level Agreements (SLAs) in defining DR expectations is analyzed. By comparing the advantages and limitations of these mechanisms, this paper provides insights into how organizations can select the most appropriate DR strategy based on their specific business needs and risk tolerance. The study concludes by offering recommendations for optimizing disaster recovery plans in cloud environments to ensure business continuity and data protection in the face of unexpected disruptions.