Cloud Database Scalability: Meeting Modern Enterprise Demands

Kondapalli, Sai Venkata (2025) Cloud Database Scalability: Meeting Modern Enterprise Demands. World Journal of Advanced Engineering Technology and Sciences, 15 (1). pp. 2278-2290. ISSN 2582-8266

[thumbnail of WJAETS-2025-0469.pdf] Article PDF
WJAETS-2025-0469.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download ( 601kB)

Abstract

Cloud database technologies have emerged as a critical solution for enterprises grappling with explosive data growth and unpredictable workload patterns. This comprehensive article examines how modern cloud database systems address enterprise scalability challenges through dynamic resource allocation, distributed architectures, and automated management capabilities. Further, we deep dive into the core scalability technologies, including horizontal and vertical scaling approaches, automatic scaling mechanisms, and distributed database architectures that enable organizations to handle exponentially growing datasets. The article further analyzes various database service models (DBaaS, cloud-native distributed databases, self-managed deployments), resource optimization strategies (connection pooling, query optimization, workload management), and crucial implementation considerations for successful cloud database migrations. Through real-world examples across industries, this article demonstrates how properly implementing these technologies allows enterprises to balance performance requirements with cost optimization while maintaining the business agility required in today's data-driven landscape.

Item Type: Article
Official URL: https://doi.org/10.30574/wjaets.2025.15.1.0469
Uncontrolled Keywords: Enterprise Cloud Database Scalability; Horizontal Vs. Vertical Scaling Strategies; Distributed Database Architectures; Serverless Database Technology; Multi-Region Database Deployment; Database-As-A-Service (Dbaas); Workload Management Optimization; Cloud Database Migration Planning; Data Lifecycle Management; Predictive Auto-Scaling Mechanisms
Depositing User: Editor Engineering Section
Date Deposited: 04 Aug 2025 16:21
Related URLs:
URI: https://eprint.scholarsrepository.com/id/eprint/3249