COST-EFFICIENT CLOUD DATA WAREHOUSING: AN INTEGRATED FRAMEWORK FOR RESOURCE OPTIMIZATION AND ENVIRONMENTAL SUSTAINABILITY

Abstract

Cloud-based data warehousing platforms have revolutionized enterprise data management, offering unprecedented scalability and flexibility. However, organizations face significant challenges in managing operational costs while maintaining optimal performance and environmental sustainability. This article presents a comprehensive framework for cost optimization in cloud data warehousing environments, integrating resource management, data lifecycle strategies, and artificial intelligence. The article examines the implementation of automated resource allocation, workload scheduling, query optimization techniques, and advanced sustainability metrics beyond traditional LEED certification. Through an analysis of green computing initiatives and environmental impact assessment frameworks, the article explores how organizations can achieve both operational efficiency and environmental responsibility. The article also investigates the crucial role of user education and behavioral changes in achieving sustainable cost reduction. Through detailed case studies and empirical analysis, we demonstrate how organizations can successfully balance performance requirements with cost optimization goals while maintaining strong environmental stewardship. The findings indicate that a multi-faceted approach combining technical optimization, user empowerment, AI-driven automation, and green computing practices can lead to substantial cost savings while promoting environmental sustainability in cloud data warehousing operations. This article contributes to the growing body of knowledge on sustainable cloud computing and provides practical guidelines for organizations seeking to optimize their data warehouse investments while minimizing their environmental footprint.