Published Paper Details:

Disaster Recovery (DR) and High Availability (HA) are core features of modern database management systems, particularly within the SQL Server environment, because they are the backbone of business continuity in instances of system crashes, data corruption, or any other disaster. While numerous options exist, for example, geo-replication, Failover Clustering, and Always On Availability Groups, significant challenges exist to optimize these systems in a myriad of environments like hybrid cloud, big data, healthcare, and small and medium-sized enterprises (SMEs). The gap in existing literature is on integration of these technologies with new-age cloud-based technologies, the utilization of automated recovery processes, and the determination of cost-effective approaches for small-sized organizations that have limited IT infrastructures. Current research highlights the increasing demand for multi-cloud and hybrid disaster recovery solutions that reduce downtime and data loss. Although such solutions are scalable and cost-effective, they also pose challenges of performance, latency, and coordination of distributed systems. Additionally, most studies concentrate on large enterprises and do not consider the unique requirements of SMEs that need more cost-effective yet reliable disaster recovery solutions. Additionally, there is little research on fully automating disaster recovery processes, which can significantly impact reducing recovery time and human error. Moreover, the healthcare industry needs special consideration in regulatory compliance, data protection, and confidentiality in disaster recovery processes. This paper aims at filling these gaps through an analysis of the latest HA and DR technologies, such as their adaptability across industries, the integration of automated recovery solutions, and cost-effective approaches for small, medium, and large enterprises. Important findings will guide improved disaster recovery functions for SQL Server in a way that permits minimal downtime and data coherence across various operating environments.