How do I add a dbt source asset to an asset group in dagster?

To add a dbt source asset to an asset group in Dagster, you can define the dbt source as a Dagster asset and specify the

group_name

property. Here's a step-by-step guide based on the documentation: 1. Create a file called

sources.yml

inside the

models/

directory of your dbt project. 2. Define your dbt source in the

sources.yml

file and include metadata that specifies the corresponding Dagster asset key. For example:

yaml version: 2  sources:   - name: jaffle_shop     tables:       - name: raw_customers         meta:           dagster:             asset_key: ["raw_customers"] # This metadata specifies the corresponding Dagster asset for this dbt source.

3. In your Dagster code, define the dbt source as a Dagster asset and assign it to a group by using the

@asset

decorator with the

group_name

property:

python @asset(group_name="your_asset_group_name") def dbt_source_asset():     # Your logic to load the dbt source data     pass

By following these steps, you can organize your dbt source assets into groups, which helps manage and visualize them in the Dagster UI. Remember to replace

"your_asset_group_name"

with the actual name of the asset group you want to use. Please note that the code example provided here is a general representation based on the documentation's guidance. You will need to adapt it to fit the specifics of your dbt project and Dagster assets.

Is there a way to do it without referencing dagster in dbt code (i.e. without the 'meta' step)

Yes, you can integrate dbt models into Dagster without referencing Dagster in your dbt code. You can achieve this by using the

dagster-dbt

library, which provides a set of abstractions to work with dbt within Dagster. Here's how you can do it: 1. Install the

dagster-dbt

library if you haven't already:

shell pip install dagster-dbt

2. Use the

dbt_cli_resource

to configure your dbt project within Dagster. This resource allows you to run dbt commands within Dagster without modifying your dbt code. 3. Define a Dagster asset that represents the dbt model. You can use the

dbt_cli_resource

to run dbt commands such as

dbt run

or

dbt test

and specify the models you want to run. 4. Add the dbt asset to an asset group by specifying the

group_name

parameter in the

@asset

decorator. Here's an example of how you might define a dbt asset in Dagster and add it to an asset group:

python from dagster import asset, AssetGroup from dagster_dbt import dbt_cli_resource  # Configure the dbt CLI resource dbt_cli = dbt_cli_resource.configured(     {         "project_dir": "path/to/your/dbt/project",         "profiles_dir": "path/to/your/dbt/profiles",     } )  # Define a Dagster asset for a dbt model @asset(required_resource_keys={"dbt"}, group_name="your_asset_group_name") def dbt_model(context):     # Run the dbt model     context.resources.dbt.run(models=["your_dbt_model_name"])  # Create an asset group with the dbt asset asset_group = AssetGroup([dbt_model], resource_defs={"dbt": dbt_cli})

In this example,

your_dbt_model_name

should be replaced with the actual name of your dbt model, and

your_asset_group_name

should be replaced with the name of the asset group you want to use. The

dbt_model

asset will be part of the specified asset group and can be managed within Dagster's asset catalog. By using the

dagster-dbt

library, you can integrate dbt with Dagster without having to add Dagster-specific metadata to your dbt project. This approach keeps your dbt codebase clean and focused on dbt-specific configurations.