The evolution of data integration: AI-driven ETL and modern data lakes
Year: 2025
Type: article
Abstract: 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.
Author: Pavan Surya Sai Koneru
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4409418222