Hi everyone! Is there an option to configure a

dbt_resource

in job config, eg. in dagit? In documentation I've seen only in code configuration using

dbt_cli_resource.configured

method. But this requires to set some configs inside the code. I want to set

project_dir

and

dbt_executable

outside of code, to have an option to switch between dev and prod envs without chaning code.

Hi Cezary. I think you should be able to omit the

configured

portion of the

dbt_cli_resource

, but that does mean that you will need to fill in the appropriate section of config at the job level. For example:

# run config yaml
resources:
  my_dbt_resource:
    config:
      project_dir: "my/project/dir"
      dbt_executable: "my/dbt/executable"

Thank you so much! This is exactly what I was looking for! 😀 In code I've passed

dbt_cli_recource

from

dagster_dbt

directly into

resource_defs

in

to_job

graph method.

# my_graph.py
from dagster_dbt import dbt_cli_resource

@op(required_resource_keys={"my_dbt_resource"})
def dbt_run(context):
    context.resources.my_dbt_resource.run()

@graph
def my_graph():
    dbt_run()

my_job = my_graph.to_job(
    resource_defs={
        'my_dbt_resource': dbt_cli_resource
    }
)

# config yml:
resources:
  my_dbt_resource:
    config:
      project_dir: "my/project/dir"
      dbt_executable: "my/dbt/executable"

@Dagster Bot discussion Configure

dbt_resource

in job config

Question in the thread has been surfaced to GitHub Discussions for future discoverability: https://github.com/dagster-io/dagster/discussions/8543