Optimizing cloud data warehousing performance using artificial intelligence techniques

The data warehouses in the clouds are now necessary to handle and analyze the extensive enterprise data. Nevertheless, conventional optimization methods are usually not effective in dealing with the growing masses of data, non-steady workloads, and complex queries. Such restrictions cause bottlenecks in performance, inefficient use of resources, and higher operational costs of the new data environment based on clouds. This paper will explore how artificial intelligence can be used to improve the performance and efficiency of cloud data warehouse systems. The study suggests an AI-enhanced optimization system that incorporates query optimization through machine learning, smart workload control, and predictive resource provision in the cloud data warehouse systems. The specific framework proposed uses an adaptive learning model to study query patterns, predict workloads on the system, and dynamically assign computational resources to enhance overall system performance. As experimental testing on benchmark workloads shows, the suggested AI-powered technique can increase query processing performance, lower query latency, and improve the use of resources by a considerable margin over the existing optimization mechanisms. The outcomes show that there is a significant benefit in scalability and operational performance of large-scale cloud data warehouse settings through intelligent automation using AI-based methods. The present work presents an in-depth AI-centered optimization system of cloud data warehousing and offers an understanding of how machine learning methodologies can be implemented in the contemporary data infrastructure to solve the problem of performance in big data analytics. The results reveal the opportunity of AI-assisted optimization to assist in creating more efficient, scalable, and cost-efficient cloud data management systems.