In DBT, it is possible to write Python code to perform custom operations on data models, such as finding the upstream and downstream models of a given model.
An upstream model is a model that is used as an input to another model, while a downstream model is a model that uses another model as input. Understanding these relationships is crucial in maintaining the integrity of data models and ensuring that data is transformed and aggregated correctly.
To find the upstream and downstream models using Python in DBT, we can use the dbt library, which provides a Python interface to the DBT CLI. The dbt library has a method dbt.run_operation that can be used to run a DBT CLI command and return the result as a Python object.
To find the upstream models of a given model, we can run the following code:
import dbt.run
result = dbt.run_operation("deps", "--models", "model_name")
upstream_models = result["upstream_nodes"]
Similarly, to find the downstream models of a given model, we can run the following code:
import dbt.run
result = dbt.run_operation("deps", "--models", "model_name")
downstream_models = result["downstream_nodes"]
The result of the dbt.run_operation method is a dictionary that contains information about the upstream and downstream models, including their names and the relationships between them.
In conclusion, using Python to get upstream and downstream models in DBT is a straightforward process that allows data engineers to quickly and easily find the relationships between models and ensure the integrity of their data transformations. The dbt library provides a simple and powerful interface to the DBT CLI, making it easy for data engineers to perform complex operations on their data models.
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