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# ask-ai
j

Joram Sbrisny

03/04/2024, 3:11 PM
@Scout: How can i run a dbt macros with dagster an build an upstream dependcy to this macro
s

Scout

03/04/2024, 3:11 PM
In Dagster, dbt macros are not directly materialized as assets because they are not models, seeds, or snapshots; they are pieces of reusable code that can be invoked within dbt models or other macros. However, you can create a software-defined asset in Dagster that invokes a dbt model which, in turn, uses a macro. This asset can then be used to establish upstream dependencies. To build an upstream dependency to a dbt macro, you would typically: 1. Define a Dagster asset that represents the dbt model that uses the macro. 2. Ensure that any upstream dependencies that need to be completed before the dbt model runs are defined as upstream Dagster assets. 3. Materialize the assets using the Dagster UI or the command line, ensuring that the upstream dependencies are respected. Here's a high-level example based on the documentation:
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python from dagster_dbt import dbt_cli_resource, dbt_run_op  @asset(required_resource_keys={"dbt"}) def my_dbt_model(context):     # This is where you would invoke the dbt model that uses your macro.     # The dbt_run_op is a pre-built operation provided by Dagster for running dbt commands.     # You would use the `models` argument to specify the particular model(s) you want to run.     result = dbt_run_op(context, models=["my_model_that_uses_macro"])     return result
In this example,
my_model_that_uses_macro
would be the name of your dbt model that uses the macro. The
dbt_run_op
is a Dagster operation that runs the dbt command, and you can specify which models to run using the
models
argument. To define upstream dependencies, you would use the
@asset
decorator's dependencies argument to specify other assets that must be materialized before `my_dbt_model`:
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python from dagster import asset  @asset def upstream_asset():     # Logic for upstream asset     pass  @asset(dependencies={"upstream_asset": upstream_asset}) def my_dbt_model(context):     # Logic for the dbt model that uses the macro     pass
In this case,
upstream_asset
is an asset that must be materialized before
my_dbt_model
can run. When you materialize
my_dbt_model
, Dagster will ensure that
upstream_asset
has been materialized first. Remember that the actual invocation of the macro happens within the dbt model itself, not directly through Dagster. Dagster orchestrates the run of the dbt model which includes the macro. Please note that the code examples provided are based on the documentation's description of using dbt with Dagster and are meant to illustrate the concepts. You would need to adapt the code to fit your specific dbt project and Dagster setup.
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