AI-Powered ETL Automation for Compliant Data Migration

Abstract

Data governance poses an increasingly complex challenge for organizations that operate on multiple heterogeneous systems, including various clouds, to ensure compliance with numerous regulations, like GDPR or HIPAA. Whereas traditional approaches to ETL (Extract–Transform–Load) have problems with manual schema mapping, weak transformation logic, and traceability, AutoETL-C provides a solution to these problems with a suggested depreciation of ETL, as it incorporates AI to deliver an automated and policy-compliant ETL. The AI to automate ETL creates a more seamless experience for users, combining (i IJAIBDCMS-V6I4P116  neural schema matching via domain-adapted language models, (ii) hybrid transformation through rules and learning, automated constraint discovery, and invasive trace law, (iii) data quality monitoring with continuous anomaly detection, (iv) and full-coverage lineage capture, including all governance and compliance limited controls. AutoETL-C was tested on three migration scenarios—core-to-lakehouse for banking, EHR modernization for healthcare, and ERP-to-warehouse for retail—public and synthetic datasets as well as anonymized enterprise data were used. AutoETL-C outperformed its manually configured ETL, template-based ETL and ML-assisted mapping, setting a new industry standard while proving compliance with Shelby Control Model documentation, HIPAA Security Rule controls, and data maps. Overall, results suggest that migration risk and time-to-value can be substantially improved with augmented, privacy-aware data engineering