Build Your Semantic Layer with dbt Projects and Semantic Views

Data teams today struggle to maintain consistent metric definitions across their organization. Business logic gets duplicated in dozens of dashboards and queries, leading to conflicting numbers and eroded trust. If you're a data engineer or analytics engineer looking to create a single source of truth for your business metrics using a code-first workflow, this lab is for you.

Snowflake Semantic Views let you define business metrics, dimensions, and relationships as native database objects, and the new dbt_semantic_view package from Snowflake Labs lets you manage them directly within your dbt project. This means your semantic layer is version-controlled, tested, and deployed alongside the rest of your data transformations with no context switching required.

In this Hands-On Lab, we will:
 

• Set up a dbt project in a Snowflake Workspace
• Install the dbt_semantic_view package
• Build base data models
• Author semantic views that define metrics and dimensions using the materialized='semantic_view' configuration
• Test and deploy them with dbt build
• Query the resulting semantic views using Snowflake's SEMANTIC_VIEW() SQL syntax

By the end of this session, you'll know how to model a semantic layer as code in dbt, deploy semantic views to Snowflake, query them with standard SQL, and integrate them with downstream tools like Cortex Analyst and BI platforms, giving your organization consistent, governed metrics from a single pipeline.