dbt Semantic View Converter

A dbt package that provides a custom materialization for creating Snowflake semantic views directly from dbt semantic model configurations.

Overview

This dbt package bridges the gap between dbt's Semantic Layer and Snowflake's native semantic views, allowing you to:

• Transform dbt semantic model definitions into Snowflake semantic views using native dbt workflows
• Maintain consistent business logic across your semantic layer
• Leverage dbt's dependency management, testing, and documentation features
• Use familiar dbt materialization patterns for semantic views

🚀 Quick Start

Installation

Add this package to your dbt project's packages.yml:

packages:
  - git: "https://github.com/sfc-gh-ahuck/dbt_semantic_view_converter.git"
    revision: main

Then run:

dbt deps

Important: After installing, make sure to run dbt deps and then dbt parse to ensure all macros are properly loaded.

Usage

1. Define Semantic Models

Create semantic models in your schema.yml:

semantic_models:
  - name: orders
    description: "Order fact table"
    model: ref('dim_orders')
    entities:
      - name: order_id
        type: primary
      - name: customer_id
        type: foreign
    dimensions:
      - name: order_date
        type: time
        type_params:
          time_granularity: day
      - name: order_status
        type: categorical
    measures:
      - name: order_total
        agg: sum
      - name: order_count
        expr: 1
        agg: sum

2. Create Semantic View Models

Create a model file using the semantic_view materialization:

-- models/semantic_views/orders_semantic_view.sql
{{ config(
    materialized='semantic_view',
    schema='semantic_layer'
) }}

SELECT 1 as placeholder

3. Run dbt

dbt run --models orders_semantic_view

This creates a Snowflake semantic view based on your semantic model definition!

⚙️ Configuration

Package Variables

Configure the package in your dbt_project.yml:

vars:
  dbt_semantic_view_converter:
    semantic_views_database: "{{ target.database }}"
    semantic_views_schema: "semantic_layer"
    copy_grants: true

Model-Level Configuration

{{ config(
    materialized='semantic_view',
    schema='my_semantic_layer',
    database='analytics',
    tags=['semantic', 'daily']
) }}

📖 Example Output

The package generates Snowflake CREATE SEMANTIC VIEW statements like:

CREATE OR REPLACE SEMANTIC VIEW analytics.semantic_layer.orders
  COMMENT = 'Order fact table'
  TABLES (
    orders AS dim_orders
      PRIMARY KEY (order_id)
  )
  RELATIONSHIPS (
    to_customer_id AS
      semantic_model (customer_id) REFERENCES customer
  )
  FACTS (
    orders.order_total AS order_total
  )
  DIMENSIONS (
    orders.order_date AS DATE_TRUNC('DAY', order_date),
    orders.order_status AS order_status
  )
  METRICS (
    orders.total_order_total AS SUM(order_total),
    orders.total_count AS COUNT(*)
  )
  COPY GRANTS;

🧪 Testing

Test the package with the included example models:

# Run example semantic views
dbt run --models semantic_views

# Check generated objects in Snowflake
SHOW SEMANTIC VIEWS;

🔧 Advanced Usage

Multiple Semantic Models

Define multiple semantic models and create corresponding view files:

# schema.yml
semantic_models:
  - name: orders_semantic_view
    # ... configuration
  - name: customers_semantic_view  
    # ... configuration

-- models/semantic_views/orders_semantic_view.sql
{{ config(materialized='semantic_view') }}
SELECT 1 as placeholder

-- models/semantic_views/customers_semantic_view.sql
{{ config(materialized='semantic_view') }}
SELECT 1 as placeholder

Custom Schema and Database

{{ config(
    materialized='semantic_view',
    schema='custom_semantic_layer',
    database='custom_analytics_db'
) }}

🔍 Troubleshooting

"'get_semantic_model_config' is undefined"

If you encounter this error when importing the package:

1. Ensure dbt deps is run: Make sure you've run dbt deps after adding the package
2. Parse the project: Run dbt parse to load all macros
3. Check package installation: Verify the package appears in dbt_packages/ directory
4. Verify profile: Ensure your dbt profile is properly configured

"Invalid enum value: avg in enum AggregationType"

dbt's semantic layer doesn't support avg aggregation. Use these instead:

• sum, count, count_distinct, max, min, median, sum_boolean

"No semantic model configuration found"

This error occurs when:

• The semantic model name doesn't match your model file name
• The semantic model isn't defined in schema.yml
• The schema.yml file isn't in the same directory as your model

Time Spine Requirements

If you see "The semantic layer requires a time spine model", add this to your project:

-- models/time_spine.sql
{{ config(materialized='table') }}
{{ dbt_utils.date_spine(
    datepart="day",
    start_date="cast('2020-01-01' as date)",
    end_date="cast('2025-01-01' as date)"
) }}

📚 Documentation

For detailed usage instructions, see docs/usage_guide.md.

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Transform your dbt semantic models into Snowflake semantic views with native dbt workflows! 🚀