For decades, data engineering has revolved around building reliable pipelines to extract, transform, and load (ETL) data, ensuring that business analysts and data scientists have access to trustworthy datasets. The role has always focused on scale, reliability, and speed. But with the rise of large language models (LLMs), the traditional definition of ETL and analytics is shifting. Generative AI is no longer just a research curiosity; it’s becoming a powerful co-pilot in modern data platforms.
This article explores how LLMs are impacting ETL and analytics, the opportunities and challenges they create, and what the near future may look like. To make things practical, we’ll refer to a real-world case in which a global retailer used LLMs to automate parts of its data transformation and analytics pipeline.