Adaptive Hybrid Data Structures for Dynamic Workload Optimization in Big Data Environments

The growth rate of data in modern computing environments had posed great challenges in data structure optimization and management thereby making this study to re-evaluate traditional approaches in the context of Big Data dynamics. This research focuses on a novel “adaptive hybrid data structures for dynamic workload optimization in Big Data environments” through intelligent structural transitions and workload- aware algorithms. This investigation seeks to tackle crucial issues related to data structure optimization using a three-tier architecture that is designed with an adaptive algorithm strategy and evaluated with a dataset of 1.5TB with different workload distributions. The response from the experimental scrutiny shows that the performance of the proposed framework has been improved by 47% in query response time (p<0.001), memory overhead has been lower by 35% (CI: ±1.8%), 38% reduction in CPU Utilization, and 99.997% availability. It achieved and maintained a throughput of 10,000 TPS at 99.999% data consistency across the entire system. When the system is tested with traditional methods, its performance is better when the system ambiguity is high which allows the system to equally adjust to changes in workload patterns across different time intervals. This sets the stage for the ability of the framework to greatly improve on amount of Big Data being processed while minimizing system instability and resources usage achieving state of the art in adaptive data structure performance for large data processing systems.