AI-Powered Data Vault 2.0 Modeling for Business Intelligence and Automation

This study explores the innovative application of Artificial Intelligence (AI) in revolutionizing data engineering practices, specifically focusing on the enhancement of the Data Vault modeling process in the context of big data environments. By leveraging the TPC-DS data set, a widely recognized industry benchmark that simulates complex, large-scale data scenarios, the research investigates the capabilities of ChatGPT in automating, accelerating, and refining the creation of Data Vault models. The methodology includes an iterative approach where ChatGPT generates models using various prompt engineering techniques. Comparative analysis is conducted against traditional modeling methods, emphasizing critical factors such as scalability to massive data sets, the speed and efficiency of model creation, precision in handling diverse data formats, and the AI’s adaptability to dynamic schema changes. The study also examines ChatGPT’s ability to seamlessly integrate new, high-volume data sources into existing models while maintaining performance in big data processing contexts. The findings aim to uncover insights into the practical viability of ChatGPT as a transformation tool for data practitioners, highlighting its potential to ensure higher accuracy and streamline complexities inherent in large-scale Data Vault modeling. This exploration serves as a foundational step toward understanding the broader implications of AI in advancing the state of modern big data warehousing and analytics.