This guide will provide step-by-step instructions for how to build a simple CI/CD pipeline for Snowflake with GitHub Actions. My hope is that this will provide you with enough details to get you started on your DevOps journey with Snowflake and GitHub Actions.

DevOps is concerned with automating the development, release and maintenance of software applications. As such, DevOps is very broad and covers the entire Software Development Life Cycle (SDLC). The landscape of software tools used to manage the entire SDLC is complex since there are many different required capabilities/tools, including:

This guide will focus primarily on automated release management for Snowflake by leveraging the GitHub Actions service from GitHub. Additionally, in order to manage the database objects/changes in Snowflake I will use the schemachange Database Change Management (DCM) tool.

Let's begin with a brief overview of GitHub and schemachange.


This guide assumes that you have a basic working knowledge of Git repositories.

What You'll Learn

What You'll Need

You will need the following things before beginning:

  1. Snowflake
  2. A Snowflake Account.
  3. A Snowflake Database named DEMO_DB.
  4. A Snowflake User created with appropriate permissions. This user will need permission to create objects in the DEMO_DB database.
  5. GitHub
  6. A GitHub Account. If you don't already have a GitHub account you can create one for free. Visit the Join GitHub page to get started.
  7. A GitHub Repository. If you don't already have a repository created, or would like to create a new one, then Create a new respository. For the type, select Public (although you could use either). And you can skip adding the README, .gitignore and license for now.
  8. Integrated Development Environment (IDE)
  9. Your favorite IDE with Git integration. If you don't already have a favorite IDE that integrates with Git I would recommend the great, free, open-source Visual Studio Code.
  10. Your project repository cloned to your computer. For connection details about your Git repository, open the Repository and copy the HTTPS link provided near the top of the page. If you have at least one file in your repository then click on the green Code icon near the top of the page and copy the HTTPS link. Use that link in VS Code or your favorite IDE to clone the repo to your computer.

What You'll Build


GitHub provides a complete, end-to-end set of software development tools to manage the SDLC. In particular GitHub provides the following services (from GitHub's Features):

GitHub Actions

"GitHub Actions makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want" (from GitHub's GitHub Actions). GitHub Actions was first announced in October 2018 and has since become a popular CI/CD tool. To learn more about GitHub Actions, including migrating from other popular CI/CD tools to GitHub Actions check out Learn GitHub Actions.

This guide will be focused on the GitHub Actions service.

Database Change Management (DCM) refers to a set of processes and tools which are used to manage the objects within a database. It's beyond the scope of this guide to provide details around the challenges with and approaches to automating the management of your database objects. If you're interested in more details, please see my blog post Embracing Agile Software Delivery and DevOps with Snowflake.

schemachange is a lightweight Python-based tool to manage all your Snowflake objects. It follows an imperative-style approach to database change management (DCM) and was inspired by the Flyway database migration tool. When schemachange is combined with a version control tool and a CI/CD tool, database changes can be approved and deployed through a pipeline using modern software delivery practices.

For more information about schemachange please see the schemachange project page.

Open up your cloned repository in your favorite IDE and create a folder named migrations. In that new folder create a script named V1.1.1__initial_objects.sql (make sure there are two underscores after the version number) with the following contents:


Then commit the new script and push the changes to your GitHub repository. Assuming you started from an empty repository, your repository should look like this:

GitHub repository after first change script

Action Secrets in GitHub are used to securely store values/variables which will be used in your CI/CD pipelines. In this step we will create secrets for each of the parameters used by schemachange.

From the repository, click on the Settings tab near the top of the page. From the Settings page, click on the Secrets tab in the left hand navigation. The Actions secrets should be selected. For each secret listed below click on New repository secret near the top right and enter the name given below along with the appropriate value (adjusting as appropriate).

Secret name

Secret value












When you're finished adding all the secrets, the page should look like this:

GitHub Actions Secrets after setup

Action Workflows represent automated pipelines, which inludes both build and release pipelines. They are defined as YAML files and stored in your repository in a directory called .github/workflows. In this step we will create a deployment workflow which will run schemachange and deploy changes to our Snowflake database.

name: snowflake-devops-demo

# Controls when the action will run. 
      - main
      - 'migrations/**'

  # Allows you to run this workflow manually from the Actions tab

    runs-on: ubuntu-latest

      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
      - name: Checkout repository
        uses: actions/checkout@v2

      - name: Use Python 3.8.x
        uses: actions/setup-python@v2.2.1
          python-version: 3.8.x

      - name: Run schemachange
          SF_ACCOUNT: ${{ secrets.SF_ACCOUNT }}
          SF_USERNAME: ${{ secrets.SF_USERNAME }}
          SF_ROLE: ${{ secrets.SF_ROLE }}
          SF_WAREHOUSE: ${{ secrets.SF_WAREHOUSE }}
          SF_DATABASE: ${{ secrets.SF_DATABASE }}
          SNOWFLAKE_PASSWORD: ${{ secrets.SF_PASSWORD }}
        run: |
          python --version
          echo "Step 1: Installing schemachange"
          pip install schemachange
          echo "Step 2: Running schemachange"
          schemachange -f $GITHUB_WORKSPACE/migrations -a $SF_ACCOUNT -u $SF_USERNAME -r $SF_ROLE -w $SF_WAREHOUSE -d $SF_DATABASE -c $SF_DATABASE.SCHEMACHANGE.CHANGE_HISTORY --create-change-history-table

Finally, click on the green Start commit button near the top right of the page and then click on the green Commit new file in the pop up window (you can leave the default comments and commit settings). You'll now be taken to the workflow folder in your repository.

A few things to point out from the YAML pipeline definition:

In this step we will manually run the new Actions workflow for the first time. This will deploy the first database migration script we created in step 4.

GitHub Actions workflow run page

To view the details of a run, click on the name of specific run (you may have to refresh the Actions page for it to show up in the list). From the run overview page click on the deploy-snowflake-changes-job job and then browse through the output from the various steps. In particular you might want to review the output from the Run schemachange step.

GitHub Actions workflow run output

Now that your first database migration has been deployed to Snowflake, log into your Snowflake account and confirm.

Database Objects

You should now see a few new objects in your DEMO_DB database:

Take a look at the contents of the CHANGE_HISTORY table to see where/how schemachange keeps track of state. See the schemachange README for more details.

Query History

From your Snowflake account click on the History tab at the top of the window. From there review the queries that were executed by schemachange. In particular, look at the Query Tag column to see which queries were issued by schemachange. It even tracks which migration script was responsible for which queries.

In this script you will create your second database migration and have GitHub Actions automatically deploy it to your Snowflake account (no need to manually run the workflow this time)!

Open up your cloned repository in your favorite IDE and create a script named V1.1.2__updated_objects.sql (make sure there are two underscores after the version number) in the same migrations folder with the following contents:


Then commit the new script and push the changes to your GitHub repository. Because of the continuous integration trigger we created in the YAML definition, your workflow should have automatically started a new run. Open up the workflow, click on the newest run, then click on the deploy-snowflake-changes-job job and browse through the output from the various steps. In particular you might want to review the output from the Run schemachange step.

So now that you've got your first Snowflake CI/CD pipeline set up, what's next? The software development life cycle, including CI/CD pipelines, gets much more complicated in the real-world. Best practices include pushing changes through a series of environments, adopting a branching strategy, and incorporating a comprehensive testing strategy, to name a few.

Pipeline Stages

In the real-world you will have multiple stages in your build and release pipelines. A simple, helpful way to think about stages in a deployment pipeline is to think about them as environments, such as dev, test, and prod. Your GitHub Actions workflow YAML file can be extended to include a stage for each of your environments. For more details around how to define stages, please refer to Workflow syntax for GitHub Actions.

Branching Strategy

Branching strategies can be complex, but there are a few popular ones out there that can help get you started. To begin with I would recommend keeping it simple with GitHub flow (and see also an explanation of GitHub flow by Scott Chacon in 2011). Another simple framework to consider is GitLab flow.

Testing Strategy

Testing is an important part of any software development process, and is absolutely critical when it comes to automated software delivery. But testing for databases and data pipelines is complicated and there are many approaches, frameworks, and tools out there. In my opinion, the simplest way to get started testing data pipelines is with dbt and the dbt Test features. Another popular Python-based testing tool to consider is Great Expectations.

With that you should now have a working CI/CD pipeline in GitHub Actions and some helpful ideas for next steps on your DevOps journey with Snowflake. Good luck!

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