Analyzing the Evolution of Data Warehousing Architectures in Response to Real-Time Analytics Demands and Heterogeneous Data Streams
Abstract
The shift toward real-time decision-making and the exponential growth of heterogeneous data streams have transformed traditional data warehousing paradigms. This paper analyzes the architectural evolution of data warehouses to accommodate the increased complexity of data sources and the velocity of analytical demands. We examine the transition from monolithic Enterprise Data Warehouses (EDW) to modern hybrid and cloud-native architectures such as Data Lakehouses and streaming warehouses. Through a synthesis of published research and industry trends, this study highlights the challenges, benefits, and implications of this evolution. Findings suggest that responsive, flexible, and scalable warehouse models are imperative for sustaining competitive analytics in modern enterprises.