Set up dbt models & orchestrate dbt runs

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1. Set up new Mage project

Read the setup guide to initialize a new project and start the Mage tool locally.

For the rest of this tutorial, we’ll use the project name demo_project.

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2. Set up dbt project

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Create dbt project

1. Open Mage and go to the terminal page: http://localhost:6789/terminal

2. Initiate your dbt project using the init command (for this tutorial, we’ll use the dbt project name demo):

   cd demo_project/dbt
   dbt init demo -s
   touch demo/profiles.yml
   

   For more information on creating a dbt project, read their documentation.

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3. Create standard pipeline

1. Go to the Mage dashboard and click the button + New pipeline and select the option labeled Standard (batch).
2. Click the Pipeline settings icon in the left pane, and change its name to dbt demo pipeline, then click the Save pipeline settings button.

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4. Create dbt profile for database connections

1. On the left side of the page in the file browser, expand the folder demo_project/dbt/demo/.
2. Click the file named profiles.yml.
3. Paste the following credentials in that file:

   demo:
     target: dev
     outputs:
       dev:
         dbname: xyleviup
         host: queenie.db.elephantsql.com
         password: edSrMWH7Mww-lTKpp-jPHX9sYSNLy7LG
         port: 5432
         schema: dbt_demo
         type: postgres
         user: xyleviup
   

4. Save the profiles.yml file by pressing Command (⌘) + S.
5. Close the file by pressing the X button on the right side of the file name dbt/demo/profiles.yml.

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5. Add data loader block to pipeline

1. Click the + Data loader button, select Python, then click API.

2. In the popup dialog Data loader block name, change its name to load data, then click the Save and add block button.

3. Paste the following code in that block:

   import io
   import pandas as pd
   import requests
   from pandas import DataFrame
   
   
   @data_loader
   def load_data_from_api(**kwargs) -> DataFrame:
       url = 'https://raw.githubusercontent.com/mage-ai/datasets/master/restaurant_user_transactions.csv'
   
       response = requests.get(url)
       df = pd.read_csv(io.StringIO(response.text), sep=',')
       df.columns = ['_'.join(col.split(' ')) for col in df.columns]
       return df
   

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6. Add dbt model block to pipeline

1. Under the data loader block you just added, click the button dbt model, then click the option Single model.

2. In the file browser that pops up, click the file named my_second_dbt_model.sql under the folders demo/models/example/.

   1. This will add 2 dbt blocks to your pipeline: 1 for the dbt model named my_first_dbt_model and the 2nd for the dbt model named my_second_dbt_model.

   2. The model named my_first_dbt_model was added to the pipeline because my_second_dbt_model references it.

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6a. Edit dbt model my_first_dbt_model

1. In the dbt block named my_first_dbt_model, next to the label Target at the top, choose dev in the dropdown list. You can also check Manually enter target, and enter dev in the input field.

2. Paste the following SQL into the dbt model named my_first_dbt_model:

   WITH source_data AS (
       SELECT 1 AS id
       UNION ALL
       SELECT 2 AS id
   )
   
   SELECT *
   FROM source_data
   

3. Run the dbt model block by pressing the play button on the top right of the block or by pressing Command + Enter.

4. You should see a preview of the query execution logs. To see the query results, click the Expand table link at the bottom right corner.

5. After previewing the results, in the top right corner of the block, click on the triple dots to reveal a dropdown menu.
6. Under the dropdown menu, click the option Run model. This command will execute the dbt run command and create the table in your data source.

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6b. Edit dbt model my_second_dbt_model

1. In the dbt block named my_second_dbt_model, next to the label Target at the top, choose dev in the dropdown list. You can also check Manually enter target, and enter dev in the input field.

2. Paste the following SQL into the dbt model named my_second_dbt_model:

   SELECT
       a.*
       , b.*
   FROM {{ ref('my_first_dbt_model') }} AS a
   
   LEFT JOIN {{ source('mage_demo', 'dbt_demo_pipeline_load_data') }} AS b
   ON 1 = 1
   
   WHERE a.id = 1
   

   dbt sources

   When a dbt model depends on an upstream block that isn’t a dbt model, a source for that block is automatically added to the demo_project/dbt/demo/models/example/mage_sources.yml file.

   Read more about dbt sources in their documentation.

3. Run the dbt model block by pressing the play button on the top right of the block or by pressing Command + Enter.

4. You should see a preview of the query execution logs. To see the query results, click the Expand table link at the bottom right corner.

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7. Add test for dbt model

1. On the right side of the screen, click the tab labeled Terminal.
2. Create a new dbt test file by running the following command:

   touch demo_project/dbt/demo/tests/test_my_second_dbt_model.sql
   

3. On the left side of the page in the file browser, expand the folder demo_project/dbt/demo/tests/ and click the file named test_my_second_dbt_model.sql. If you don’t see it, refresh the page.
4. Paste the following SQL in the file:

   SELECT id
   FROM {{ ref('my_second_dbt_model') }}
   GROUP BY id
   HAVING (id = 0)
   

5. Read more about dbt tests in their documentation.

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8. Execute pipeline end-to-end

1. Click the name of the pipeline in the header breadcrumbs to go back to the detail page.

2. Create a new trigger with a type Schedule and a Frequency once. For more details, follow these steps.

3. After your trigger is created, click the Start trigger button at the top of the page.

4. The pipeline will eventually fail because a dbt test failed. This means everything is working as expected.

5. Open the file demo_project/dbt/demo/models/example/schema.yml and remove the tests named unique under both models. Your file should look like this:

   
   version: 2
   
   models:
     - name: my_first_dbt_model
       description: "A starter dbt model"
       columns:
         - name: id
           description: "The primary key for this table"
           tests:
             - not_null
   
     - name: my_second_dbt_model
       description: "A starter dbt model"
       columns:
         - name: id
           description: "The primary key for this table"
           tests:
             - not_null
   
   

6. Click on the Failed button next to the pipeline run and click Retry run. It should complete running successfully after a few minutes.

Congratulations! You’ve created a data pipeline that orchestrates your dbt models.

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Support

If you get stuck, run into problems, or just want someone to walk you through these steps, please join our Slack.