AI-Orchestrated Data Warehousing: Enhancing Scalability and Query Optimization in Cloud Databases

Abstract

This paper presents an AI-Orchestrated Data Warehousing framework designed to enhance scalability and query optimization in cloud databases through the integration of predictive modeling, reinforcement learning, and auto-tuning mechanisms. Traditional data warehouses struggle to maintain performance under dynamic workloads due to static optimization and reactive scaling strategies. The proposed system embeds AI orchestration at the core of the data pipeline, enabling proactive resource provisioning, adaptive query planning, and self-learning optimization. Using a hybrid architecture deployed in a containerized cloud environment, the study demonstrates substantial performance gains—reducing average query latency by 44%, improving throughput by 68%, and increasing resource utilization efficiency by 33%. The reinforcement learning agent continuously adjusts execution plans, while predictive models forecast resource needs, achieving a balance between cost and performance. Experimental evaluation confirms that AI orchestration delivers consistent elasticity, reduced overhead, and autonomous performance tuning. The findings establish AI-orchestrated warehousing as a scalable, intelligent, and energy-efficient paradigm for next-generation cloud analytics.