LLM-Powered ETL: A Practical Blueprint for Modern Data Engineering

Synopsis

LLM-Powered ETL: A Practical Blueprint for Modern Data Engineering, by Anand Butani, addresses the critical gap between the executive mandate to "add AI" and the daily realities faced by data engineers. This book provides a comprehensive, end-to-end guide for integrating Large Language Model (LLM) capabilities directly into existing data pipelines, moving beyond treating generative AI as a peripheral initiative. Butani demonstrates how to embed LLM-driven data profiling, schema mapping, and quality remediation into popular frameworks like Airflow, Dagster, and dbt, as well as cloud-native workflows. By grounding every technique in open-source examples, measurable quality metrics, and clear cost-benefit analyses, the book aims to transform AI hype into repeatable, auditable engineering patterns. Readers will learn to automate time-consuming ETL tasks, deliver explainable data quality fixes, and significantly reduce maintenance backlogs. This empowers data engineers and architects to accelerate pipeline development, enhance data reliability, and free up valuable engineering cycles for higher-value analytics and model development. The ultimate goal is to shift data teams from reactive, labor-intensive maintenance to proactive, AI-assisted excellence, building organizational confidence in generative AI by proving its tangible ROI and enabling the creation of self-healing, trustworthy data platforms that scale efficiently.