AUTOMATING DISASTER RECOVERY AND BACKUP STRATEGIES FOR ENHANCED RESILIENCE IN LARGE-SCALE RETAIL IT SYSTEMS

Abstract

This paper discusses disaster recovery (DR) and automation of backup processes for big retail IT infrastructure, focusing on the significance of the processes towards ensuring operational continuity and data consistency. The review of 41 papers highlights key strategies, including setting backup schedules based on policies, using Infrastructure as Code (IaC) for deployment, automating failover and failback tasks, and incorporating Artificial Intelligence (AI) and Machine Learning (ML) to make backups more efficient. These approaches reduce downtime, improve system redundancy, and simplify recovery processes. In addition, the paper describes how automation can help optimize disaster recovery efficiency and effectiveness, particularly in the advanced retail environments that demand high availability and quick recovery time. The research also examines the issues and limitations of automating DR and backup in big retail IT environments, including scalability problems, real-time processing problems, and integration problems with existing IT infrastructure. It also details the tools and platforms used to facilitate these automated processes, presenting a general overview of the current state of practice and research. By projecting the possibility of automation in disaster recovery, this article provides real-world advice for companies willing to maximize their IT resilience and guarantee ongoing business activities despite interruptions.