Make dbt magical

First, select the simple_dbt_python_pipeline by double-clicking the row from the list of pipelines this will take you directly to the editor. You can also single click, then select the “code” icon from the side nav.

Scroll to the bottom-most cell and click Execute with all upstream blocks.

You just:

• Seeded a dbt model
• Performed a dbt transformation
• Took the transformed data and performed a Python transformation
• Wrote the data to a Postgres source

To see the output, you can use a querying tool (like DataGrip or psql) to the locally hosted Postgres database and query public.analytics.cur_customers.

Select the dynamic_dbt_pipeline by double-clicking the row from the list of pipelines— this will take you directly to the editor. You can also single click, then select the “code” icon from the side nav.

Scroll to the bottom-most cell and click Execute and run all upstream blocks.

You just:

• Pulled a GitHub repo and wrote it to your local Mage directory
• Wrote a demo profiles.yaml file that interpolated environment variables from your instance
• Compiled dbt build to write data to your local postgres database.

To run this pipeline, click the name of the pipeline to open the Triggers page and select Run @once to trigger a pipeline run. Select the logs icon to see the run in realtime!

Now, in addition to pulling the external Jaffle Shop demo and writing the necessary profiles, you’ll perform dbt build, which will: run models, test tests, snapshot snapshots, and seed seeds. 🥳

To see the output, you can use a querying tool (like DataGrip or psql) to the locally hosted Postgres database. To learn more about the Jaffle Shop demo, check out the repo.