globusjournal

With the rapid growth of cloud computing, efficient data management has become a critical factor in ensuring optimal performance, scalability, and cost efficiency. Organizations increasingly rely on cloud-based solutions to store, process, and analyze vast amounts of data, requiring innovative strategies to balance computational power with resource allocation. This study explores key methodologies for optimizing cloud data management, including data partitioning, automated load balancing, storage tiering, and resource orchestration. It examines the impact of distributed storage architectures, real-time data processing, and AI-driven automation on performance improvements. Furthermore, the research highlights cost optimization techniques, such as pay-as-you-go pricing models, deduplication, and compression strategies, to reduce operational expenses without compromising efficiency. By analyzing case studies and industry best practices, this study provides insights into how businesses can enhance their cloud infrastructure while maintaining flexibility, security, and cost-effectiveness.

Keywords: Cloud Data Management, Performance Optimization, Scalability, Cost Efficiency, Distributed Storage, Load Balancing, Resource Orchestration, Data Partitioning, AI in Cloud Computing, Cloud Cost Optimization.