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Building a Blueprint with dbt Core (Data Build Tool)#


In this tutorial, you'll walk through the steps required to set up dbt to run in the cloud, on Shipyard. We will be creating a Blueprint that can be re-used by multiple team members and updated in the background. This tutorial is only in Python.

By the end of the tutorial, you'll be able to:

  • Set up a Blueprint using Python
  • Successfully run dbt on Shipyard
  • Share data models with your organization
  • Run multiple instances of dbt simultaneously
  • Integrate a data model into your Fleets

With this tutorial, it is assumed that:

  • You have already set up an integration with Github.
  • You are running this tutorial for the first time using the contents of the jaffle-shop tutorial or a repository with your own model definitions.

If you need to reference an example dbt setup, you can view the code at our dbt-tutorial repository.

For more information, read our blog post that covers Getting Started with dbt. You can also visit for additional information.


  1. Add this script to the root directory of the GitHub repository where your dbt models live, with the file name of
  2. Move your profiles.yml file to the root directory of the GitHub repository where your dbt models live.


  1. Click Blueprints on the side navigation bar.
  2. Click the Add Blueprint button in the top right.

Step 1 - Select A Language#

Click on Python. You'll be immediately redirected to the next step.

Step 2 - Create Blueprint Variables#

Click the + icon to create a new Blueprint variable. You should see a screen that looks like this:

Our code for dbt has only 1 variables that we expect to receive. For a detailed overview of each of these fields, read more about Blueprint Variables.

DBT CLI Command#

  1. Set the Display Name to DBT CLI Command
  2. Set the Reference Name to DBT_COMMAND
  3. Leave the Variable Type set to Alphanumeric.
  4. Set the Default Value to dbt run.
  5. Check the box for Required?
  6. Leave the placeholder empty.
  7. Set the Tooltip to Enter the CLI command you'd like to run. Supports running multiple commands successively with &&
  8. Click Add Variable.

Give your Blueprint a Description that will help others in your team understand what dbt code is being accesed.

At this point, your screen should look something like this.

Click Preview this Blueprint to verify how everything will look and feel to a user.

Once you've verified that everything is set up correctly, go ahead and click Next Step.

Step 3 - Provide Your Code#

  1. Click the Git section of the page.
  2. Select your dbt repository from the dropdown. Ours is named dbt-tutorial
  3. Select the branch or tag that you want to git checkout at runtime. Ours is named main.
  4. Keep the Git Clone Location as New Folder with Repo Name (default)
  5. On the right-hand side of the screen, enter ${SHIPYARD_CLONE_LOCATION}/ into the File to Run field.

Once these steps are complete, your screen should look exactly like this.

Once you've verified that everything has been set up correctly, click Next Step in the bottom right.

Step 4 - Requirements#

Environment Variables#

  1. Click the + icon next to Environment Variables 3x to add three new variables.
  2. Set the first variable's Name to BIGQUERY_CREDS and Value to your json credentials.
  3. Set the second variable's Name to BIGQUERY_KEYFILE and Value to bigquery_creds.json.
  4. Set the third variable's Name to DBT_PROFILES_DIR and Value to .

The value field will always show โ€ขโ€ขโ€ขโ€ขโ€ขโ€ขโ€ข as you type. This is because Environment Variables are commonly used for passwords and secrets. You can always reveal what you've written by clicking the eye icon.


  1. Click the + icon next to Packages.
  2. Set the first Package Name to dbt and the version to ==0.19.1

Your screen should look similar to this:

Once you're done, go ahead and click the Next Step button at the bottom of the screen.

Step 5 - Settings#

  1. Under the State section, select Everyone.
  2. Under the Information section:
    1. Give your Blueprint the name of dbt Tutorial Run.
    2. Give your Blueprint the Synopsis of Quickly execute dbt (data build tool) CLI commands such as dbt run, dbt test, and more. Syncs with up-to-date code on Github.
  3. Set the Icon to
  4. Leave the Guardrails section defaults of None and ASAP.

Your Blueprint should look like this:

  1. Click the Save & Finish button at the bottom of the screen.

You've successfully set up dbt as a Blueprint.

Now anyone in your organization can use the Blueprint to run your data models. We're going to test our Blueprint to validate that everything runs correctly.

Step 6 - Setting Up a Vessel#

  1. Click Use this Blueprint on the success screen.

At this point, you should be on a screen that looks like this:

  1. Enter dbt run into the DBT CLI Command field.
  2. Click Next Step.
  3. On the Settings step:
    1. Name your Vessel dbt run test
    2. Select either Playground or Testing for the Project
    3. Click Save & Finish
  4. Immediately Click Run Your Vessel

Step 7 - Review the Results#

You should be immediately redirected to the actively running Vessel Log. Within the Log you'll be able to see all of the dbt models and their output for the sample data.

You should also be able to see the resulting tables and views in your database of choice.

Congratulations on setting up a dbt Blueprint! You now have a repeatable solution that can be used again and again for all of your data models.

What Comes Next#

Now that you've successfully worked your way through this tutorial, there's a lot of additional things that you can try out on your own with this knowledge.

Run Different Commands#

Can you set up new Vessels with the same Blueprints

Set up Triggers#

dbt works best when it's running on a consistent schedule or immediately after new data has loaded. Try adding Triggers to your dbt Vessels or including them as part of a Fleet.

Upload Logs#

Every time you run dbt, a log file is either created or updated. This works great when you're running dbt locally, but not as great on Shipyard where the default behavior is to wipe any generated files after runtime.

Try to set up a Fleet that uploads the generated dbt.log file in /logs/ to your Cloud storage platform of choice.