DATA PARTITIONING AND LOAD BALANCING IN DISTRIBUTED DATABASES: A REVIEW

managing large-scale data efficiently. This review explores the fundamental challenges of data partitioning and load balancing in distributed database environments, emphasizing the impact of various partitioning strategies, including range-based, hash-based, and hybrid approaches. Additionally, the study examines load balancing techniques, differentiating between static and dynamic methods, to optimize workload distribution and prevent system bottlenecks. Recent advancements in artificial intelligence (AI) and machine learning (ML) have been leveraged to enhance resource allocation, while bio-inspired optimization algorithms such as Ant Colony Optimization (ACO) and Genetic Algorithms (GA) contribute to adaptive partitioning solutions. Moreover, the integration of block chain technology offers secure and decentralized approaches to load balancing, mitigating single points of failure. The role of the Internet of Things (IoT) in distributed databases is also discussed, particularly in real-time data flow and intelligent load management. This review provides a comprehensive analysis of existing strategies, their advantages, limitations, and potential research directions to enhance performance, scalability, and fault tolerance in distributed database systems.