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#ask-ai
Title
# ask-ai
c

Cowthulhu

02/25/2024, 7:31 AM
@Scout I'm getting an error when attempting to run DBT models through Dagster. The models work fine when I run them manually via dbt build. The error states that my configuration is missing a dbt_cloud.yml file, but that file is only required when using DBT cloud (which I am not, I'm using DBT core).
s

Scout

02/25/2024, 7:31 AM
Based on the documentation, it seems you are trying to integrate dbt Core with Dagster and encountering an error related to a missing
dbt_cloud.yml
file. The documentation provides guidance on using dbt Cloud with Dagster, but if you are using dbt Core, the process is slightly different and does not require a
dbt_cloud.yml
file. For dbt Core integration, you would typically use the
dagster-dbt
package and configure resources for dbt CLI execution. The configuration would involve specifying the
project_dir
and potentially the
profiles_dir
among other settings, depending on your dbt project setup. Here's an example of how you might configure a dbt CLI resource, as per the documentation:
Copy code
python from dagster_dbt import dbt_cli_resource  dbt_cli = dbt_cli_resource.configured({     "project_dir": "path/to/your/dbt/project",     "profiles_dir": "path/to/your/dbt/profiles",     # other configurations as needed })
You would then use this resource in your Dagster job to run dbt commands. If you are seeing an error about a missing
dbt_cloud.yml
file, it could be due to a misconfiguration or an incorrect reference in your Dagster setup. Ensure that your Dagster job or pipeline is correctly configured to use dbt Core and not dbt Cloud. Double-check your resource definitions and any environment variables that might be pointing to dbt Cloud instead of dbt Core. If the error persists and you are certain that your configuration is correct for dbt Core, it might be helpful to look at the specific error message and stack trace to understand where the issue is occurring. If the documentation does not provide a clear solution, you may need to debug further or seek assistance from the Dagster community, such as through GitHub Issues or Discussions, to resolve the issue.
c

Cowthulhu

02/25/2024, 7:32 AM
@Scout, what would an incorrect reference look like?
s

Scout

02/25/2024, 7:32 AM
An incorrect reference in your Dagster setup that might cause an error about a missing
dbt_cloud.yml
file could take several forms. Here are a few examples of what an incorrect reference might look like: 1. Incorrect Resource Configuration: If you have mistakenly configured a resource that is intended for dbt Cloud usage instead of dbt Core, it might look for
dbt_cloud.yml
. Ensure that your resource configuration is for dbt CLI, not dbt Cloud. 2. Wrong Environment Variable: If you have environment variables that are set for dbt Cloud (such as
DBT_CLOUD_API_TOKEN
), but you are using dbt Core, this could lead to confusion in the setup. Make sure that your environment variables match the tool you are using. 3. Incorrect Import: If you have imported a module or a function that is specific to dbt Cloud integration instead of dbt Core, it could be looking for dbt Cloud-specific files. 4. Misconfigured Job or Pipeline: If your Dagster job or pipeline has a solid or op that is configured to interact with dbt Cloud instead of dbt Core, it might expect a
dbt_cloud.yml
file. Here's an example of what a correct dbt Core resource configuration should look like:
Copy code
python from dagster_dbt import dbt_cli_resource  dbt_cli = dbt_cli_resource.configured({     "project_dir": "path/to/your/dbt/project",     "profiles_dir": "path/to/your/dbt/profiles",     # other configurations as needed })
And here's an example of what an incorrect reference that is intended for dbt Cloud might look like:
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python from dagster_dbt import dbt_cloud_resource  # This is incorrect for dbt Core  dbt_cloud = dbt_cloud_resource.configured({     "dbt_cloud_config": "path/to/dbt_cloud.yml",  # This should not be here for dbt Core     # other configurations as needed })
To resolve the issue, you should: - Review your Dagster assets (resources, solids, ops, jobs, pipelines) to ensure they are configured for dbt Core. - Check your codebase for any accidental imports or references to dbt Cloud-specific configurations or functions. - Verify that your environment variables and any other configuration files are appropriate for dbt Core. If you continue to encounter issues, you may want to enable more verbose logging to get additional context on where the error is occurring, or reach out to the Dagster community for support.