Revolutionizing Data Warehousing through AI-Driven Data Engineering

Abstract

The advent of artificial intelligence (AI) has initiated a paradigm shift in data engineering, 
particularly in data warehousing. Traditional data warehousing systems, though effective, face 
significant challenges in handling the increasing complexity and volume of modern data. This 
paper explores the integration of AI-driven techniques into data warehousing to address these 
challenges and enhance overall system performance. AI-powered solutions offer innovative 
approaches to data processing, management, and analysis, providing substantial improvements in 
scalability, efficiency, and adaptability. By leveraging machine learning algorithms, automated 
data integration, and predictive analytics, AI-driven data warehousing can significantly optimize 
data workflows, reduce latency, and enhance decision-making capabilities. This study evaluates 
various AI techniques applied to data warehousing, including automated data cleansing, intelligent 
data integration, and adaptive resource allocation. The results demonstrate that AI-driven 
approaches not only streamline data management but also facilitate more accurate and timely 
insights. This paper presents a comprehensive overview of the advancements and applications of 
AI in data warehousing, discussing the benefits, challenges, and future directions for research. The 
findings highlight the transformative potential of AI in revolutionizing data warehousing, offering 
new pathways for handling complex data environments and supporting data-driven decisionmaking.