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DISASTER RECOVERY IN LARGE-SCALE DATABASES: DESIGNING EFFECTIVE FAILOVER AND BACKUP STRATEGIES

By
Harsha Vardhan Reddy Kavuluri Orcid logo
Harsha Vardhan Reddy Kavuluri

Lead Oracle, Postgres, Cloud Database Administrator (Contractor for Deloitte) United States

Abstract

Within the database infrastructures of large-scale enterprises and government organizations, preserving swift and dependable disaster recovery processes continues to be particularly challenging with increasing data volumes and greater complexity in systems. This study conducts an evaluative analysis of advanced failover and backup methods, comparing traditional cold standby models to multi-region, transaction-aware replication architectures. Controlled fault injection across OLTP and OLAP systems yielded results showing 64% reduction in average Recovery Time Objective (RTO) from 430 seconds to 155 seconds. Under write-heavy workloads, RPO drift was improved by over 70%; decreasing from 8.1 seconds in legacy systems to 2.3 seconds in the systems with adaptive replicas. There was also an improvement of 19% in the success rate of transaction rollbacks, whereas predictive failure detection reached 91% accuracy in forecasting excessive write queue formation. Additionally, the study demonstrates a reduction in cost-efficiency of modern architecture, showcasing a 47% decline in recovery cost per gigabyte of restored data. These results purposefully outline the significant operational and technical benefits accompanying the implementation of software-defined disaster recovery techniques within high-availability environments.

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Citation

This is an open access article distributed under the  Creative Commons Attribution Non-Commercial License (CC BY-NC) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 

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