HintsToday

Here’s a complete blueprint to help you develop and maintain CI/CD pipelines using GitHub for automated deployment, version control, and DevOps best practices in data engineering — particularly for Azure + Databricks + ADF projects.

🚀 PART 1: Develop & Maintain CI/CD Pipelines Using GitHub

✅ Technologies & Tools

🛠️ Sample CI/CD Pipeline for a Data Platform

📁 Repo Structure

/data-platform/
├── notebooks/
│   ├── bronze/
│   ├── silver/
│   └── gold/
├── adf/
│   ├── pipelines/
│   ├── linkedservices/
│   └── triggers/
├── infrastructure/
│   └── main.bicep (or main.tf)
├── tests/
├── .github/workflows/
│   └── deploy.yml
└── README.md

🔄 Sample GitHub Actions CI/CD Workflow (.github/workflows/deploy.yml)

name: CI-CD Deploy ADF + Databricks

on:
  push:
    branches: [main]

env:
  DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }}
  DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
  RESOURCE_GROUP: "rg-data-eng"
  ADF_NAME: "adf-production"

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v3

    - name: Deploy ADF via ARM Template
      uses: azure/CLI@v1
      with:
        inlineScript: |
          az login --service-principal -u ${{ secrets.AZURE_CLIENT_ID }} \
            -p ${{ secrets.AZURE_CLIENT_SECRET }} \
            --tenant ${{ secrets.AZURE_TENANT_ID }}
          az deployment group create \
            --resource-group $RESOURCE_GROUP \
            --template-file adf/main-template.json \
            --parameters adf/parameters.json

    - name: Sync Notebooks to Databricks
      run: |
        pip install databricks-cli
        databricks workspace import_dir ./notebooks /Repos/data-pipeline -o

🧠 PART 2: Collaborate with Cross-Functional Teams to Drive Data Strategy & Quality

🔹 Strategies for Collaboration

🔹 Key Practices

• Maintain shared data dictionaries
• Automate schema checks & column validations
• Create Slack/email alerts for pipeline failures
• Use Unity Catalog for lineage + access control
• Implement contracts between layers (Bronze → Silver → Gold)

✅ Sample Data Quality Check (PySpark)

from pyspark.sql.functions import col, isnan, count

df.select([count(when(col(c).isNull() | isnan(c), c)).alias(c) for c in df.columns]).show()

🏗️ PART 3: DevOps & Infrastructure-as-Code (IaC) Best Practices

✅ DevOps Best Practices for Data Engineering

✅ Terraform Example: Provision Databricks Workspace + Unity Catalog

resource "azurerm_databricks_workspace" "this" {
  name                = "dbx-dev"
  location            = "East US"
  resource_group_name = var.resource_group_name
  sku                 = "premium"
}

resource "databricks_catalog" "finance" {
  name     = "finance_catalog"
  comment  = "Managed by Terraform"
}

✅ Use Infrastructure Environments (Terraform or Bicep)

Use a single codebase with different parameters/configs per environment

✅ Final Checklist