AI-Enhanced Data Warehousing: Optimizing ETL Processes for Real-Time Analytics

Abstract

In the rapidly evolving landscape of big data, the integration of artificial intelligence (AI) into data warehousing has emerged as a transformative approach to optimizing Extract, Transform, Load (ETL) processes for real-time analytics. This paper explores the synergies between AI technologies and traditional data warehousing techniques, focusing on how AI can enhance the efficiency and effectiveness of ETL operations. By employing machine learning algorithms, the paper demonstrates how data quality can be improved through automated anomaly detection, intelligent data transformation, and adaptive data loading strategies. Furthermore, the integration of natural language processing (NLP) enables intuitive querying and analysis, facilitating user engagement and enhancing decision-making. Through case studies and practical implementations, this study illustrates the significant impact of AI on reducing latency, improving data accuracy, and enabling actionable insights in real time. The findings indicate that organizations leveraging AI-enhanced data warehousing can achieve substantial competitive advantages by harnessing the power of timely and accurate data-driven decisions