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Get the guideIn this tutorial, we will walk you through the process of deploying dbt to production using GitHub Actions. This will help you automate your dbt deployment process, ensuring that your data models are always up-to-date and tested.
dbt (data build tool) is an open-source transformation tool that helps data analysts and engineers transform raw data into clean, well-structured datasets. GitHub Actions is a CI/CD (Continuous Integration and Continuous Deployment) service provided by GitHub, which allows you to automate various tasks, including deploying code to production.
Follow these steps and look at these code examples to help set up dbt deployment with GitHub Actions:
Crete a profiles.yml file at the root of your repository to set up a dbt profile. This file will contain the necessary configuration for your data warehouse.
name: my_profile
target: dev
outputs:
dev:
type: [data_warehouse_type]
account: [account]
user: [user]
password: [password]
database: [database]
schema: [schema]
Replace the placeholders with your data warehouse information. This file will be used by dbt to connect to your data warehouse.
Create a credential for GitHub Actions to authenticate access to the data warehouse. This can be done by adding the necessary secrets to your GitHub repository.
Create a GitHub Actions job that runs on a cron schedule to run dbt on a schedule. This job will be responsible for running dbt commands and deploying your models to production.
name: Deploy dbt
on:
schedule:
- cron: '0 0 * * *'
workflow_dispatch:
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install dbt
- name: Run dbt
run: dbt run --profiles-dir . --project-dir .
env:
dbt_PROFILES_DIR: ${{ secrets.dbt_PROFILES_DIR }}
This example sets up a scheduled job that runs every day at midnight. Adjust the cron schedule according to your needs.
Create a pull request every time there is a change to the code base to run dbt on a merge. This ensures that your models are always tested and up-to-date.
Here are some tips for improving dbt CI/CD:
Secoda is a data cataloging and lineage tracking platform that can integrate with dbt and GitHub Actions to provide better visibility and understanding of your data models. By integrating Secoda with your dbt deployment process, you can:
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