dbt_semantic_view — What's That AI

Snowflake Semantic View dbt Package

Professional dbt macros and integration tests for building, dropping, and renaming Snowflake Semantic Views. This package lets you materialize Semantic Views via dbt and reference them from downstream models.

At a glance

Materialization: semantic_view
Warehouse: Snowflake
dbt Compatibility: dbt 1.x

Quickstart

Follow these steps on macOS/Linux with Python 3 installed. No prior dbt installation is required.

Clone and enter the repo

git clone https://github.com/Snowflake-Labs/dbt_semantic_view.git
cd dbt_semantic_view/

Create an isolated Python environment and install dependencies

python3 -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install dbt-snowflake

Configure Snowflake credentials (env vars)
Set the following environment variables for the integration profile. For username/password auth use SNOWFLAKE_TEST_AUTHENTICATOR=snowflake.

export SNOWFLAKE_TEST_ACCOUNT=<account>
export SNOWFLAKE_TEST_USER=<user>
export SNOWFLAKE_TEST_PASSWORD=<password>
export SNOWFLAKE_TEST_AUTHENTICATOR=<authenticator>   # e.g. snowflake | externalbrowser
export SNOWFLAKE_TEST_ROLE=<role>
export SNOWFLAKE_TEST_DATABASE=<database>
export SNOWFLAKE_TEST_WAREHOUSE=<warehouse>
export SNOWFLAKE_TEST_SCHEMA=<schema>

Run integration tests

cd integration_tests/
dbt deps --target snowflake
dbt build --target snowflake

Usage in your dbt project

Add to packages.yml:

packages:
  - package: Snowflake-Labs/dbt_semantic_view

Create a model using the Semantic View materialization:

{{ config(materialized='semantic_view') }}
TABLES(
  {{ source('<source_name>', '<table_name>') }},
  {{ ref('<another_model>') }}
)
[ RELATIONSHIPS ( relationshipDef [ , ... ] ) ]
[ FACTS ( semanticExpression [ , ... ] ) ]
[ DIMENSIONS ( semanticExpression [ , ... ] ) ]
[ METRICS ( semanticExpression [ , ... ] ) ]
[ COMMENT = '<comment>' ]
[ COPY GRANTS ]

Reference a Semantic View from another model:

{{ config(materialized='table') }}
select *
from semantic_view(
  {{ ref('<semantic_view_model>') }}
  [ { METRICS <metric> | FACTS <fact_expr> } ]
  [ DIMENSIONS <dimension_expr> ]
  [ WHERE <predicate> ]
)

Note on documentation persistence (persist_docs)

At this time, dbt-driven documentation persistence for Semantic Views (persist_docs) is not supported by this package. Enabling persist_docs and adding model or column descriptions will not affect Semantic Views.

Inline COMMENT syntax within the Semantic View DDL is supported and will be applied by Snowflake. For example:

CREATE OR REPLACE SEMANTIC VIEW <name>
  TABLES ( ... COMMENT = '...' )
  [ FACTS ( ... COMMENT = '...' ) ]
  [ DIMENSIONS ( ... COMMENT = '...' ) ]
  [ METRICS ( ... COMMENT = '...' ) ]
  [ COMMENT = '...' ]

We plan to revisit persist_docs support as upstream capabilities evolve.

Development

Python 3.9+ recommended
Use a venv: python3 -m venv .venv && source .venv/bin/activate
Install tooling as needed: pip install dbt-snowflake

Contributing

We welcome issues and PRs! Please:

Open an issue to discuss significant changes
Keep edits focused and include tests where possible
Follow dbt and Python best practices

License

Apache License 2.0. See LICENSE for details.

README