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Scalable Enterprise Intelligence: Cloud-Based Data Warehouses And Big Data Analytics
Dr. Lars Johansson , KTH Royal Institute of Technology, SwedenAbstract
The contemporary landscape of data management is characterized by unprecedented growth in volume, variety, and velocity of information, compelling enterprises to adopt sophisticated data warehousing solutions capable of integrating multifaceted analytical frameworks. This research critically investigates cloud-based and hybrid data warehousing architectures, emphasizing their operational, strategic, and technical implications within enterprise ecosystems. The study synthesizes existing literature, including classical frameworks, contemporary cloud solutions, and emergent trends in big data analytics, to construct a comprehensive understanding of the mechanisms that underpin effective data warehousing deployment. Using a rigorous analytical lens, the research examines Amazon Redshift as a model for modern cloud data warehousing, exploring its scalability, performance optimization strategies, and integration capabilities within heterogeneous data environments (Worlikar, Patel, & Challa, 2025). The methodological approach involves a qualitative synthesis of case studies, comparative architecture analyses, and theoretical frameworks derived from both traditional and contemporary scholarship. The results reveal that strategic implementation of cloud-based data warehouses enhances operational efficiency, reduces total cost of ownership, and facilitates advanced analytics, including predictive and prescriptive modeling. However, challenges persist in data governance, latency management, and integration of multi-source heterogeneous data, necessitating ongoing research into adaptive architectures and dynamic optimization techniques. The discussion extends to critically evaluate the evolving role of cloud-native solutions in enterprise intelligence, considering organizational, technological, and policy-level factors that shape successful adoption. This study contributes a multidimensional perspective, bridging conceptual theory with applied insights, and offers recommendations for optimizing performance, governance, and cost efficiency in large-scale cloud data warehouse systems. The findings underscore the necessity for a holistic, data-centric strategy that aligns technological capabilities with organizational objectives, providing a roadmap for future research in scalable, agile, and secure data warehousing paradigms.
Keywords
Cloud Data Warehousing, Amazon Redshift, Big Data Analytics
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Copyright (c) 2025 Dr. Lars Johansson
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