Skip to main content

dbt Core Part 2 - Setting Up dbt on GitHub

In part 2 of the dbt core setup series, we will review the setup process for each of the cloud data warehouses that we discussed on part 1. Before we can begin, there are some prerequisite items that need to be addressed:

dbt Set-Up​

Fork dbt Setup from GitHub​

  1. Fork this repository. The repository contains the beginning state of a dbt project.
  2. Clone the repository locally on your computer.
  3. Open dbt_project.yml in your text editor.

dbt Project File Setup​

  1. Change the project name to soccer_538.
  2. Change the profile to soccer_538.
  3. Change model name to soccer_538.
  4. Under the soccer_538 model, add a staging and marts folder that are both materialized as views.
  5. Save your changes.

dbt Profile Setup​

  1. Open profiles.yml and update the file to contain the following code:
profiles.yml
soccer_538:
  target: dev
  outputs:
    dev:
      type: bigquery
      method: service-account
      project: dbt-demos # Replace this with your project id
      dataset: dbt_shipyard # Replace this with dbt_your_name, e.g. dbt_bob
      threads: 4
      timeout_seconds: 300
      location: US
      priority: interactive
      keyfile: "{{ env_var('BIGQUERY_KEYFILE') }}"
note

You'll note that the keyfile above is denoted as an environment variable. We will send that in as an environment variable inside of Platform to protect it from being seen.

  1. Create a new file in your root directory of your dbt project called execute_dbt.py.
  2. Paste this code block for the content of execute_dbt.py:
execute_dbt.py
import subprocess
import os
import json

bigquery_credentials = os.environ.get('BIGQUERY_CREDS')
directory_of_file = os.path.dirname(os.path.realpath(__file__))
dbt_command = os.environ.get('dbt_command', 'dbt run')

os.chdir(directory_of_file)
if not bigquery_credentials or not bigquery_credentials == 'None':
    bigquery_credentials = json.loads(bigquery_credentials)
    with open('bigquery_creds.json', 'w') as outfile:
        json.dump(bigquery_credentials, outfile)

subprocess.run(['sh', '-c', dbt_command], check=True)
  1. Save your changes, make a commit, and push your changes to GitHub.

Now that we have our sample data and dbt processes setup, we need to write our example models for the dbt job to run.

dbt Models Setup​

  1. Navigate into the models folder in your text editor. There should be a subfolder under models called example. Delete that subfolder and create a new folder called 538_football.
  2. Create two subfolders inside 538_football called staging and marts.

  1. Inside the staging folder, create a file called stg_football_matches.sql with the following query:
stg_football_matches.sql
SELECT * 
FROM dbt-demos.538_football.stg_football_matches
  1. Inside the staging folder, create a file called stg_football_rankings.sql with the following query:
stg_football_rankings.sql
SELECT * 
FROM `dbt-demos.538_football.stg_football_rankings`
  1. In the staging folder, add a file called schema.yml with the following code:
schema.yml
version: 2

models:
  - name: stg_football_matches
    description: Table from 538 that displays football matches and predictions about each match.

  - name: stg_football_rankings
    description: Table from 538 that displays a teams ranking worldwide
note

This is file is where you will be able to add tests later.

  1. In the marts folder, create a file called mart_football_information.sql with the following query:
mart_football_information.sql
WITH
  qryMatches AS (
    SELECT *
    FROM {{ ref('stg_football_matches') }}
    WHERE league = 'Barclays Premier League'
    ),
  qryRankings AS (
    SELECT *
    FROM {{ ref('stg_football_rankings') }}
    WHERE league = 'Barclays Premier League'
  ),

  qryFinal AS (
    SELECT
      qryMatches.season,
      qryMatches.date,
      qryMatches.league,
      qryMatches.team1,
      qryMatches.team2,
      team_one.rank AS team1_rank,
      team_two.rank AS team2_rank
    FROM qryMatches 
    JOIN
      qryRankings AS team_one ON
        (qryMatches.team1 = team_one.name) 
    JOIN
      qryRankings AS team_two ON
        (qryMatches.team2 = team_two.name)
  )

SELECT *
FROM qryFinal
  1. In the marts folder, add a file called schema.yml containing the following code.
schema.yml
version: 2

models:
  - name: mart_football_information
    description: Table that displays football matches along with each team's world ranking.
  1. Save the changes.
  2. Push a commit to GitHub
note

In this tutorial, we made changes using the main branch of our GitHub repository. This was done for simplicity sake and is not the best practice.

We are ready to move into Platform to run our process in the cloud.