Shanelle Roman

ETL pipelines are often the most brittle part of the analytics stack — spread across tools, written in multiple languages, and hard to operate at scale. This session shows how teams build production-grade SQL ETL on Databricks Lakehouse using a unified, SQL-first approach. We’ll cover how to ingest, transform, and manage data using declarative pipelines, apply governance and quality controls consistently, author and optimize with AI, and deliver reliable datasets directly to analytics and downstream workloads. The focus is on simplifying architecture, reducing operational overhead, and enabling faster iteration without sacrificing correctness or scale.