Autonomous ETL Pipelines: Using Generative AI to Design, Validate, and Deploy Dataflows

Abstract

The Extract, Transform, and Load (ETL) process, a cornerstone of data engineering, has traditionally been laborious, time-consuming, and error-prone, resulting in a substantial bottleneck in today's data-driven enterprises. This study investigates the paradigm shift toward autonomous ETL pipelines powered by Generative AI and Large Language Models (LLMs). Provides a comprehensive architectural framework in which prompt-driven AI agents analyze natural language needs to build pipeline logic, construct sophisticated schema mappings, and create infrastructure-as-code (IaC) templates for deployment. The study examines the integration of AI copilots into modern platforms such as Microsoft Fabric and Databricks, which enable not only initial code generation but also continuous validation of transformation logic, dynamic optimization, and self-healing of failed jobs via reinforcement learning feedback loops. The result is a scalable, adaptive dataflow system that requires minimal human intervention, dramatically increasing development speed, operational consistency, cost-efficiency, and overall observability across complex, hybrid data ecosystems. The report finishes with a discussion of the obstacles, including delusion, security, and expense, as well as future research possibilities for fully autonomous, trustworthy data management