Stop Redefining your AI Context: Snowflake Semantic Views and Google Looker

You Already Defined Your Business Logic Once — Why Does Revenue Still Get Redefined in a Notebook, Streamlit in Snowflake and AI Agents

Looker now integrates with Snowflake Semantic View. That means you can define your context in Snowflake and have it serve as the governed definition that LookML references directly. Relationships and metrics live once in Snowflake Horizon Context. Your Explores, dashboards, and Gemini-powered Conversational Analytics keep working on top of them, and so does everything else that queries Snowflake.

In this webinar and demo, product experts from Snowflake and Google Cloud will demonstrate how to build a unified semantic model from scratch.

What you will learn to build:

• A governed semantic view in Snowflake. Model entities, relationships, and metrics as real database objects with RBAC, using Semantic Studio and Semantic View Autopilot to bootstrap from SQL you already have.
• A LookML project that consumes it. Wire Looker to the in-database analytic model, then confirm an Explore and a dashboard return the same numbers as a direct Snowflake query. No definitions duplicated.
• The same definition grounding two different agents. Ask the same question in Snowflake CoCo, Snowflake CoWork and in a Gemini-powered Conversational Analytics, then watch both answers resolve through one metric definition.