The ongoing evolution of data-intensive systems underscores the critical importance of optimizing data warehousing frameworks in contemporary computational ecosystems. As organizations confront exponential growth in data volume, velocity, and heterogeneity, the design, implementation, and management of data warehouses must be reevaluated to address both operational efficiency and analytical depth. This research article synthesizes theoretical constructs, architectural paradigms, methodological advances, and quality assurance mechanisms drawn from an interdisciplinary body of literature to propose a comprehensive framework for modern data warehousing optimization. Central to this discussion is the integration of cloud-native architectures, hybrid data lake–warehouse models, multidimensional modeling strategies, and machine-driven quality monitoring methodologies. In doing so, the article interrogates prevailing scholarly debates around data warehouse functionality, extends analytical discourse on scalability and adaptability, and identifies critical gaps in current knowledge. Drawing upon both foundational and contemporary references, including Worlikar, Patel, and Challa’s seminal work on modern data warehousing recipes (2025), this article advances a nuanced, integrative perspective that supports both theoretical understanding and practical application in diverse domains.

cloud architecture, data quality, Data warehousing

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