Real-Time Data Integration in Traditional Data Warehouses: A Comparative Analysis

Abstract

Real-time data integration into traditional data warehouses has long posed significant challenges for businesses striving to remain competitive in an increasingly data-driven landscape. Historically, data warehouses were designed for batch processing, where large volumes of data were extracted, transformed, and loaded (ETL) during scheduled intervals. This approach, while reliable, often led to delays in decision-making, as organizations could only analyze historical data after the fact. With the growing need for real-time insights, the demand for integrating live data streams into these existing systems has surged. This paper explores the evolution of real-time data integration techniques in traditional data warehouses, comparing early methodologies with modern approaches like data streaming and event-driven architectures. It also evaluates the impact of these advancements on decision-making processes, operational efficiency, and overall business agility. By analyzing case studies and reviewing the technical challenges, such as latency, data consistency, and system scalability, this study sheds light on the strengths and limitations of various strategies. Ultimately, it aims to provide businesses with a clear understanding of how they can optimize their traditional data warehouses for real-time data integration, paving the way for faster, more accurate insights.