> ## Documentation Index
> Fetch the complete documentation index at: https://docs.prophecy.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# PBT on GitHub Actions

> Example usage of Prophecy Build Tool on GitHub Actions

<Callout icon="/images/icon.png" color="#FFC107">
  Available for [Enterprise Edition](/data-engineering/administration/platform/editions) only.
</Callout>

## Using PBT with GitHub Actions

Prophecy Build Tool (PBT) can be integrated with GitHub Actions to:

* validate pipelines
* build artifacts (`.jar` / `.whl`)
* run unit tests
* deploy pipelines to Databricks

<Note>
  View an [Example GitHub repository](https://github.com/prophecy-samples/external-cicd-template).
</Note>

## Prerequisites

* A Prophecy project hosted in a GitHub repository
* A Databricks workspace for deployment

## Configuration

### Environment variables

PBT requires the following:

* `DATABRICKS_HOST`
* `DATABRICKS_TOKEN`

Store the token as a GitHub Actions secret:

> Settings → Secrets → Actions → New repository secret

Then reference it in your workflow:

```yaml theme={null}
env:
  DATABRICKS_HOST: "https://<your-databricks-instance>"
  DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
```

## Example workflow (deploy on push to prod)

This workflow:

* runs on every push to `prod`
* validates, builds, and tests pipelines
* deploys artifacts to Databricks

Create the workflow file:

```
.github/workflows/exampleWorkflow.yml
```

### Workflow definition

```
name: Example CI/CD with GitHub actions

on:
  push:
    branches:
      - "prod"

env:
  DATABRICKS_HOST: "https://sample_databricks_url.cloud.databricks.com"
  DATABRICKS_TOKEN: ${{ secrets.PROD_DATABRICKS_TOKEN }}
  FABRIC_ID: "4004"  # replace with your fabric id

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v3

      - name: Set up JDK 11
        uses: actions/setup-java@v3
        with:
          java-version: "11"
          distribution: "adopt"

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: "3.9.13"

      - name: Install dependencies
        run: |
          python3 -m pip install --upgrade pip
          pip install build pytest wheel pytest-html pyspark==3.3.0 prophecy-build-tool

      - name: Validate pipelines
        run: pbt validate --path .

      - name: Build pipelines
        run: pbt build --path .

      - name: Run tests
        run: pbt test --path .

      - name: Deploy pipelines
        run: pbt deploy --path . --release-version 1.0 --project-id example_project_id
```

## What this workflow does

1. Triggers on pushes to the `prod` branch
2. Sets required environment variables for Databricks access
3. Installs Java, Python, and PBT dependencies
4. Validates pipeline syntax (`pbt validate`)
5. Builds pipelines into `.jar` / `.whl` artifacts (`pbt build`)
6. Runs unit tests (`pbt test`)
7. Deploys artifacts and jobs to Databricks (`pbt deploy`)
   * Uploads artifacts referenced in `databricks-job.json`
   * Creates or updates Databricks jobs
   * Deploys pipeline configurations to DBFS if defined

If any step fails, the workflow stops and the run is marked as failed.