Self-Tuning Data Warehouse Architectures for High Throughput Analytical Workloads

In this paper, the problem of managing large amounts of analytical workloads in the current generation of data warehouses is considered by analyzing self-tuning capabilities. Modern businesses produce considerable volumes of both structured and semi-structured data which need to be effectively processed in order to provide insights for decision-making and further analysis. Existing data warehouses require a lot of effort to be put into index definition, optimization of queries, allocating resources, partitioning data, and managing workloads. When dealing with highthroughput workloads in complex databases, manual approaches to tuning cannot guarantee consistency in performance, scalability, and effectiveness. To address this drawback, researchers began developing self-tuning data warehouse solutions to be able to detect and resolve performance issues automatically. The current research will analyze the effect of using self-tuning mechanisms on data warehouses dealing with large analytical workloads. The offered architectural framework incorporates workload monitoring, intelligent resource management, automation of physical design optimization, adaptive query processing, and machine learning-based decision making in order to create an optimized analytical environment. Workload-aware tuning policies will allow the framework to optimize storage structures, allocate and manage computing resources, and optimize query execution. Thus, the research aims at demonstrating the considerable contribution that self-tuning data warehouse architectures make in terms of enhancing query performance, effective resource management, scalability, and overall effectiveness.