BLOCKCHAIN FOR SECURE AND TRANSPARENT BIG DATA TRANSACTIONS

Data partitioning and load balancing are fundamental techniques in distributed database systems, ensuring efficient data storage, retrieval, and query execution across multiple nodes. Data partitioning strategies, including horizontal, vertical, and hybrid approaches, optimize data distribution to enhance performance, scalability, and fault tolerance. Meanwhile, load balancing dynamically allocates workloads across servers, preventing bottlenecks and improving resource utilization. Researchers have explored various adaptive algorithms and machine learning-based strategies to optimize partitioning and load balancing under dynamic workloads. However, challenges such as partitioning overhead, network latency, and data consistency remain critical areas of research. This review consolidates existing methodologies, discussing their advantages, limitations, and emerging solutions in the field. Furthermore, recent advancements in AI - driven load balancing, predictive partitioning models, and blockchain-integrated database distribution highlight new opportunities for enhancing system efficiency. By examining the current state of research, this study aims to provide insights into optimizing data distribution and workload management in distributed databases, addressing ongoing challenges while exploring future research directions.