Scalable Cloud Data Warehouses And Lakehouse Systems: Architectures For Next-Generation Decision Support

Abstract

In the contemporary era of data-driven decision-making, organizations face unprecedented challenges in the collection, management, and analysis of vast datasets. Modern data warehousing solutions have evolved from traditional relational models to cloud-based, distributed, and columnar architectures capable of handling petabyte-scale data efficiently. This paper investigates the design, implementation, and operationalization of contemporary data warehousing systems with a particular focus on Amazon Redshift as a representative cloud-based solution (Worlikar, Patel, & Challa, 2025). By synthesizing theoretical perspectives from decision support systems, business intelligence frameworks, and distributed computing paradigms, this study delineates the intricate interplay between architecture, performance, and analytical capability. Emphasis is placed on methodologies for optimizing query execution, ensuring data integrity through ACID-compliant transaction management, and leveraging advanced partitioning and indexing strategies to enhance retrieval efficiency (Apache Iceberg, 2023; Delta Lake, 2023). Furthermore, this research examines the integration of modern lakehouse architectures, including Delta Lake and Dremio Arctic, within enterprise ecosystems, highlighting the implications for scalability, concurrency control, and real-time analytics (Dremio Sonar, 2023; LakeFS, 2023). By exploring the comparative advantages of cloud-native versus on-premises data warehouses, this paper also addresses critical factors such as total cost of ownership, operational agility, and data governance. The findings offer a comprehensive framework for decision-makers and technical architects to align warehouse design with organizational intelligence objectives, thereby enabling informed, timely, and actionable insights across business domains. Ultimately, this work contributes to a nuanced understanding of modern data warehousing, situating cloud-based architectures within a continuum of technological evolution and operational efficacy while offering a roadmap for future research and development in high-performance analytics environments.