Cloud Workflows Using Azure Pipelines and Databricks

 

How I Automated My Cloud Workflows Using Azure Pipelines and Databricks

 

In one of my recent projects, I wanted to eliminate manual deployment and automate my end-to-end cloud workflow—from pushing code to triggering data jobs. I chose Azure Pipelines to handle the CI/CD process, and integrated it with Databricks to trigger data workflows after successful deployment.

 

Here’s a look into how I set it all up—and how it made development smoother and faster.

  

What I Needed to Automate

 

The project stack included:

A .NET Core API hosted on Azure App Service

Databricks notebooks for post-deployment data processing

Code stored in GitHub

Deployment and data workflow automation using Azure Pipelines

 

My goal was simple:

Every code push to the main branch should automatically build, test, deploy the API, and then trigger a Databricks job.

 

Setting Up Azure Pipelines

 

Azure Pipelines lets you define your entire CI/CD process using a YAML configuration file. Here's a simplified version of what I used:

 

azure-pipelines.yml

 

trigger:

  branches:

    include:

      - main

 

pool:

  vmImage: 'ubuntu-latest'

 

steps:

  # Restore and build .NET project

  - task: DotNetCoreCLI@2

    inputs:

      command: 'build'

      projects: '**/*.csproj'

 

  # Run unit tests

  - task: DotNetCoreCLI@2

    inputs:

      command: 'test'

      projects: '**/*Tests.csproj'

 

  # Deploy to Azure Web App

  - task: AzureWebApp@1

    inputs:

      appName: 'my-api-app'

      package: '$(System.DefaultWorkingDirectory)/**/*.zip'

 

  # Trigger Databricks job after successful deployment

  - script: az databricks jobs run-now --job-id 1234

    displayName: 'Trigger Databricks Job'

 

 

 

Azure + Databricks Integration

 

To securely connect Azure Pipelines with Databricks:

I used Azure CLI tasks to run Databricks REST API commands.

Service connections were set up with the correct Databricks access token and workspace URL.

I used the az databricks CLI to schedule job execution after deployment.

This helped me chain app deployments with real-time data processing tasks—without manual intervention.

 

 

 

What This Setup Achieved

 

✅ CI/CD for code: No more manual builds or deployments

✅ Automated testing: Tests ran automatically on each push

✅ Triggered data jobs: Databricks workflows launched after API deployment

✅ Error-free delivery: Reduced the risk of missing steps or introducing bugs in production

 

 

Key Learnings

Azure Pipelines is extremely flexible—whether you’re deploying apps, containers, or triggering external workflows.

YAML pipelines keep everything version-controlled and transparent.

Combining Azure with Databricks unlocks powerful full-stack automation for modern data apps.

 

Final Thoughts

This pipeline helped me move from slow, manual deployments to a fully automated DevOps workflow—spanning both application and data layers. Whether you're a developer, data engineer, or DevOps enthusiast, Azure Pipelines can become a powerful part of your workflow.

 

Want to know about GitHub repo of the setup? Drop a comment below—I’d love to share!