Advanced Frameworks And Optimization Strategies In Modern Cloud Data Warehousing: A Comprehensive Analysis Of Architectures, Performance, And Future Directions
Abstract
The evolution of data warehousing has marked a transformative period in the management, analysis, and strategic utilization of enterprise data assets. This research article critically examines advanced frameworks and optimization strategies in modern cloud data warehousing environments, with an emphasis on architectural paradigms, performance trade-offs, and emerging integrative technologies. Drawing on extensive literature and technical guidelines—including foundational principles articulated by Inmon and Kimball alongside contemporary cloud-oriented studies—this paper synthesizes theoretical constructs and empirical evidence to delineate effective practices in contemporary data warehousing. Key topics explored include architectural design considerations, performance optimization techniques, cost management, scalability challenges, and the integration of artificial intelligence (AI) processes within cloud data platforms. Special emphasis is given to the influential practical guidance presented in the Amazon Redshift Cookbook: Recipes for building modern data warehousing solutions (Worlikar, Patel, & Challa, 2025), which provides actionable strategies for realizing robust, scalable storage and analytics infrastructures in cloud contexts. This research highlights how traditional data warehousing concepts have been reinterpreted within cloud ecosystems, advancing both operational efficiency and analytical agility. Critical debates around trade-offs between performance and cost, as well as the implications of emerging technologies for future research trajectories, are discussed to inform practitioners and scholars alike.
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References
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