AI-DRIVEN KNOWLEDGE MANAGEMENT FOR ADAPTIVE ETL WORKFLOWS: ENHANCING DATA PROFILING, TRANSFORMATION, AND QUALITY ASSURANCE

As data-driven enterprises continue to evolve, efficient Extract, Transform, Load (ETL) workflows becomecritical for ensuring data accuracy, consistency, and usability. Traditional ETL processes struggle with adaptingto dynamic data structures, heterogeneous sources, and governance requirements. This paper explores theintegration of AI-driven knowledge management with ETL workflows, focusing on adaptive data profiling, selfadjusting transformations, and AI-powered validation techniques. We analyze emerging frameworks that leveragemachine learning, rule-based knowledge systems, and automated governance models to enhance sustainability,compliance, and efficiency in modern data ecosystems.