Abstract
The rapid proliferation of data-intensive applications has catalyzed a paradigm shift in the architecture and management of data warehouses. Modern enterprises increasingly demand systems capable of integrating diverse data sources, ensuring high performance, and maintaining robust transactional consistency. This research examines contemporary approaches to cloud-based data warehousing, emphasizing the integration of Amazon Redshift solutions with emerging data lakehouse architectures. Drawing upon canonical studies and recent empirical findings, the study critically analyzes mechanisms such as ACID-compliant table storage, distributed query engines, columnar storage formats, and petabyte-scale operational optimizations. The work contextualizes these developments within a historical trajectory that spans traditional relational data warehouses, the advent of Hadoop-based systems, and the evolution of unified query engines. Through a synthesis of theoretical frameworks, architectural evaluations, and practical deployment strategies, this article identifies key strengths and limitations inherent in current data warehouse designs. Furthermore, it highlights unresolved challenges in scalability, concurrency, and real-time analytics while proposing a structured research agenda aimed at bridging conceptual and operational gaps. Findings indicate that while cloud-native solutions like Redshift and lakehouse systems offer unprecedented flexibility and performance, nuanced considerations in schema design, partitioning strategies, and query optimization remain critical for achieving consistent operational efficiency. The study underscores the necessity for multi-faceted evaluation methodologies that integrate both quantitative performance metrics and qualitative architectural assessments. The implications extend to data-intensive sectors, informing strategic decisions regarding infrastructure investment, workload management, and the adoption of hybrid data platforms.