GenAI-Driven Semantic ETL:: Synthesizing Self-Optimizing SQL & PL/SQL - Ananthakrishnan, Vasudevan; Kondaveeti, Dharmeesh; Mohammed, Abdul Samad (Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2025)
The burgeoning complexity of data ecosystems demands intelligent, adaptive ETL (Extract, Transform, Load) solutions. This research introduces GenAI-Driven Semantic ETL, a novel framework leveraging Generative Artificial Intelligence (GenAI) to automate the synthesis of self-optimizing SQL and PL/SQL code for ETL workflows. By integrating semantic understanding of data schemas, transformation rules, and performance objectives, the system dynamically generates code that autonomously refines execution strategies based on runtime statistics, workload patterns, and data evolution. Key innovations include a context-aware GenAI engine that translates natural language requirements into optimized procedural logic and a feedback-driven optimization loop enabling continuous code adaptation. Evaluations on enterprise datasets demonstrate 40–65% reductions in ETL latency and 30–50% lower resource consumption compared to manually tuned pipelines, while minimizing human intervention. This work pioneers the fusion of generative AI with semantic reasoning to realize truly autonomous, efficient, and future-proof data integration systems.