Leveraging AI-Driven Techniques for Real-Time Data Integration and Fusion in Modern Enterprise Data Warehousing Systems

Abstract

This paper explores the transformative impact of artificial intelligence (AI) on real-time data integration and fusion within modern enterprise data warehousing systems. As organizations increasingly rely on data-driven decision-making, the need for efficient, accurate, and timely data integration becomes paramount. This study highlights various AI-driven techniques, including machine learning for schema matching, natural language processing for unstructured data integration, and predictive analytics for data quality enhancement. Additionally, the paper discusses real-time data fusion techniques utilizing stream processing frameworks and event-driven architectures. Through case studies, the research demonstrates the effectiveness of these AI-driven approaches in overcoming traditional data warehousing challenges, ultimately improving operational efficiency and decision-making capabilities. The findings underscore the critical role of AI in shaping the future of enterprise data management.