Anita Thomas

This session maps the path to the next-generation data engineering on Snowflake. Learn how engineering teams can move beyond manual infrastructure management to focus on what matters most: delivering high-value data products that fuel AI at scale. Whether you're looking to build SQL, Python, Spark or ML pipelines, this session will show you how Snowflake enables data engineers to work smarter, ship faster, and build for the AI era with Snowflake CoCo and transformation features like Snowpark, Snowpark Connect for Apache Spark, Dynamic Tables, and dbt Projects.

In this 3-demo session, you'll see what modern, AI-ready data engineering looks like in practice. First, build a fraud detection model using Snowpark's DB-API (no ETL required), with vectorized UDFs for scale, optimized inference, and org-approved package management. Next, see how to run Apache Spark natively in Snowflake Notebooks, orchestrate pipelines with Snowflake CoCo, and visually author workflows using a new Pipeline Builder drag-and-connect interface. Finally, watch how to update an existing Dynamic Table with a Cortex AI Function and ship it to production using DCM Projects with no manual handoffs, no infrastructure overhead. You'll walk away with a clear picture of how Snowflake is accelerating pipeline development with smart workflows.