The evolution of data integration: AI-driven ETL and modern data lakes

Koneru, Pavan Surya Sai (2025) The evolution of data integration: AI-driven ETL and modern data lakes. World Journal of Advanced Engineering Technology and Sciences, 15 (1). pp. 788-794. ISSN 2582-8266

The digital transformation landscape is witnessing an unprecedented evolution in data integration technologies, driven by artificial intelligence and modern data lake architectures. Traditional Extract, Transform, Load (ETL) processes are giving way to intelligent, automated systems that can handle the increasing complexity and volume of enterprise data. This transformation encompasses advanced capabilities including self-healing pipelines, automated data quality management, and dynamic schema adaptation. AI-powered ETL solutions are revolutionizing how organizations process and manage data through intelligent automation, predictive maintenance, and real-time optimization. The emergence of modern data lakes, enhanced by AI capabilities, provides organizations with flexible, scalable platforms for storing and processing both structured and unstructured data. These advancements, combined with federated learning and AI-driven governance, are enabling organizations to achieve greater operational efficiency while maintaining robust security and compliance standards.