Emanuel Zgraggen

As demand for data grows, central data teams can’t build every transformation themselves. But traditional “self-service” ETL tools often introduce new problems: logic leaves governed platforms, pipelines live in parallel runtimes, and productionization requires costly rewrites by data engineering teams. Lakeflow Designer addresses this gap by giving analysts a no-code, AI-first way to do data prep on the same Lakeflow execution model used by data engineering teams. Instead of introducing a parallel toolchain, Designer produces governed, production-grade transformations that fit directly into existing data engineering workflows. In this session, you’ll see how Lakeflow Designer: - Enables analysts to do data prep without moving data outside Databricks - Preserves centralized governance, quality, and lineage through Unity Catalog - Lets data engineers scale pipeline development by directly operationalizing designer-authored logic— without rebuilding, translating, or refactoring

For most teams, the biggest bottleneck isn't the generation of insights it is the work thats required to get the data wrangled the right way. For too long, the ability to transform data and automate workflows has been locked behind complex code, leaving business analysts dependent on overstretched data engineering teams.Lakeflow Designer changes the equation with a no-code, visual canvas powered by Genie that lets anyone go from a natural language prompt to a production-ready workflow in minutes.This breakout will cover:- How Lakeflow Designer fits into the overall Databricks ecosystem and what differentiates it from other low-code tools- How to get started and leverage natural language through Genie to build a workflow with zero previous experience- How Designer’s visual workflows translate into production-grade code that is governed, scalable, and ready for enterprise use