Integrate with CI/CD Pipeline

It is good practice to apply software engineering best practices and work with analytics as with code. Thanks to GoodData Python SDK and the API-first approach, it is simple.

In this article, you will learn how to build a simple GitHub CI/CD pipeline (e.g. GitHub Actions) that provisions analytics from one workspace to another with just a few lines of code.

Steps:

1. Prepare python script that will run in a GitHub Action.

   The script creates the new workspace production, and it takes analytics from the demo workspace and puts it into the production workspace.

    from gooddata_sdk import GoodDataSdk, CatalogWorkspace
   
    # GoodData host in the form of uri eg. "http://localhost:3000"
    host = "http://localhost:3000"
    # GoodData API token
    token = "<API_TOKEN>"
    demo_workspace_id = "demo"
    production_workspace_id = "production"
   
    sdk = GoodDataSdk.create(host, token)
   
    # Create workspace
    sdk.catalog_workspace.create_or_update(
      CatalogWorkspace(production_workspace_id, production_workspace_id)
    )
   
    # Get LDM (Logical Data Model) from demo workspace 
    declarative_ldm = sdk.catalog_workspace_content.get_declarative_ldm(
      demo_workspace_id
    )
   
    # Get analytics model (metrics, dashboards, etc.) from demo workspace 
    declarative_analytics_model = sdk.catalog_workspace_content.get_declarative_analytics_model(
      demo_workspace_id
    )
   
    # Put LDM (Logical Data Model) to production workspace 
    sdk.catalog_workspace_content.put_declarative_ldm(
      production_workspace_id, 
      declarative_ldm
    )
   
    # Put analytics model (metrics, dashboards, etc.) to production workspace
    sdk.catalog_workspace_content.put_declarative_analytics_model(
      production_workspace_id, 
      declarative_analytics_model
    )
   

2. Define workflow in YAML file.

   The workflow creates an environment for the python script from the previous step and runs the script.

   name: Deploy analytics to production
   
   on:
     workflow_dispatch:
       inputs:
         name:
           description: 'Deploy analytics to production'
           required: false
   
   jobs:
     build:
       runs-on: ubuntu-latest
       steps:
         - uses: actions/checkout@v2
         - name: Set up Python 3.8
           uses: actions/setup-python@v2
           with:
               python-version: 3.8
         - name: Install GoodData Python SDK
           run: |
             python -m pip install --upgrade pip
             pip install gooddata-sdk          
         - name: Run deploy script
           run: python deploy.py
   

3. Push and test the GitHub Action.

   You can now push the script to the root of your repository and name it deploy.py. For the YAML file, push it in the .github/workflows folder with name deploy.yml.

   If everything is done properly, you will see in the GitHub action Deploy analytics to production.

   
   

The example with CI/CD mentioned in this tutorial is simple, but you can do a lot of stuff to automate analytics with GoodData Python SDK or APIs.