HYBRID CLOUD DATABASES FOR BIG DATA ANALYTICS: A REVIEW OF ARCHITECTURE, PERFORMANCE, AND COST EFFICIENCY

Abstract

The increasing reliance on big data analytics has driven organizations to seek more flexible, scalable, and cost-effective data management solutions, with hybrid cloud databases emerging as a prominent option. Hybrid cloud databases integrate both public and private cloud environments, allowing businesses to balance the scalability and cost advantages of public clouds with the security and control provided by private clouds. This systematic review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, examines the architecture, performance, cost efficiency, security, and technological advancements of hybrid cloud databases. The study synthesizes findings from 40 peer-reviewed articles published between 2015 and 2024, focusing on key factors that affect the performance of hybrid cloud databases, such as workload distribution, network latency, and the use of advanced data analytics tools like Hadoop and Spark. The review also explores cost-saving mechanisms, including dynamic resource scaling and pay-as-you-go pricing models, which help organizations reduce infrastructure costs by up to 28%. Additionally, it discusses the security risks and privacy concerns associated with hybrid cloud environments, highlighting the effectiveness of encryption protocols and identity access management systems in mitigating data breaches by as much as 40%. Furthermore, the review identifies emerging trends in the integration of machine learning and artificial intelligence (AI), which have significantly enhanced system automation and resource optimization in hybrid cloud infrastructures, leading to performance improvements of up to 45%.