Sarcouncil Journal of Multidisciplinary

Abstract: ETL processes traditionally require complex pipelines, multiple coding languages, and considerable development effort. The advent of AI-driven query layers revolutionizes this landscape by enabling AI-driven ETL powered by generative AI. By leveraging prompts as an intuitive query interface, businesses can automate data extraction, transformation, and loading without requiring SQL or procedural scripting. This article presents an architecture that utilizes AI models trained on database querying and transformations, allowing users to define ETL workflows in natural language. Generative AI models serve as an abstraction layer, translating human-readable instructions into optimized ETL jobs. With integrations into cloud data warehouses, businesses eliminate redundancy in manual operations while achieving real-time processing. Moreover, AI-driven ETL enhances self-service analytics, making data engineering more accessible across teams. Security and governance mechanisms prevent erroneous data manipulations, ensuring consistency and reliability in pipeline operations. The introduction of AI-driven ETL using AI query layers democratizes data engineering, allowing enterprises to scale operations efficiently while reducing dependency on specialized coding expertise.