Step-by-Step Guide to Automating dbt Deployment with GitHub Actions

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Published
May 13, 2024
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In 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.

What is dbt and GitHub Actions?

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.

How To Deploy dbt Using GitHub Actions

Follow these steps and look at these code examples to help set up dbt deployment with GitHub Actions:

1. Create a profiles.yml file

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.

2. Create a credential for GitHub Actions

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.

3. Add a GitHub Actions job

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.

4. Create a pull request for code changes

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.

5. Follow Best Practices for dbt CI/CD

Here are some tips for improving dbt CI/CD:

  • Ensure that your data warehouse credentials are stored securely using GitHub Secrets.
  • Make sure your dbt project is properly configured with the correct profiles.yml file.
  • Monitor your GitHub Actions jobs and set up notifications for any failures or issues.
  • Split a single GitHub Actions job into several discrete jobs for better organization and error handling.
  • Set up a Slack notification to alert when a job fails, so you can quickly address any issues.
  • Keep your dbt project well-organized and modular, making it easier to manage and deploy.
  • Integrate with data platforms like Secoda for better data cataloging and lineage tracking.

How Does Secoda Integrate with dbt and GitHub Actions?

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:

  • Automatically catalog and document your dbt models, making it easier for your team to find and understand your data.
  • Track data lineage across your dbt models, helping you understand how data flows through your organization.
  • Monitor the health of your dbt deployment process, ensuring that your models are always up-to-date and accurate.
  • Collaborate with your team on data projects, streamlining communication and reducing the risk of errors.

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