Automated Data Warehouse Optimization Using Machine Learning Algorithms

Abstract

Optimizing data warehouse performance in today’s data-driven landscape is crucial for timely insights and decision-making. Traditional methods often rely on manual tuning and rule-based configurations, which can be time-consuming and limited in scope. This paper explores an automated approach to data warehouse optimization using machine learning algorithms to enhance query performance, resource allocation, and overall system efficiency. By leveraging machine learning, our proposed solution can predict and preemptively adjust resource requirements based on data load, query patterns, and historical usage trends, allowing for real-time adaptability. Key elements of this approach include predictive analytics for workload management, anomaly detection to identify and resolve performance bottlenecks, and resource scaling based on machine learning-driven forecasts. We use supervised and unsupervised learning algorithms tailored to identify patterns in diverse workloads, automating processes such as indexing, partitioning, and caching, which traditionally require hands-on oversight. Implementing this automated framework reduces operational costs and minimizes latency, making the data warehouse more agile and responsive to business needs. We demonstrate measurable improvements in speed, scalability, and cost efficiency through case studies and simulations. This paper presents a step forward in intelligent data warehousing, showcasing how machine learning can simplify complex optimizations, leading to better performance with minimal manual intervention and providing an adaptable, future-ready solution for data-intensive organizations.