Snowflake AI Pulse: Latest Features & Releases

Loading data is only half the job — raw tables aren't something a business user can trust or read. The gap between raw data and a production dashboard is usually a tangle of manual SQL, stale refreshes, and one-off transformations nobody can maintain. This session is for the analytics and data engineers who want a clean, automated path from raw tables to a dashboard people actually rely on.

We build the pipeline the way you'd run it in production: shaping raw data with transformations, layering it into a Silver and Gold model, and keeping it fresh automatically with Dynamic Tables so you're not hand-scheduling refreshes. Then we put a Snowsight dashboard on top of the curated result. Each layer refreshes on its own, so the dashboard stays current without manual work.

In this Hands-on Lab, we will build an end-to-end pipeline live in a SQL notebook inside your own Snowflake account:

• Transforming raw tables into clean, typed datasets
• Building a curated Silver and Gold layer
• Automating refresh with Dynamic Tables
• Creating a Snowsight dashboard on the curated data

Attendees leave with a working raw-to-dashboard pipeline in their own account — transformed datasets, a curated Gold layer that refreshes automatically with Dynamic Tables, and a live Snowsight dashboard. They'll have the SQL and the pattern to repeat it for any reporting use case.