Introduction

Azure Logic Apps and Azure Data Factory are powerful services that can automate and streamline data workflows. In this blog, I'll walk you through the steps to create a Logic App that connects with Azure Data Factory and triggers a pipeline run.

Azure Logic Apps is a cloud service that helps you schedule, automate, and orchestrate tasks, business processes, and workflows. Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating data movement and transforming data at scale.

By integrating Logic Apps with Data Factory, you can automate the triggering of ADF pipelines, creating a seamless data workflow.

Steps to create a logic app that triggers an ADF pipeline run

Design the logic app workflow

  • Go to the Logic Apps Designer.
  • Choose a trigger for your Logic App. For example, you can use an HTTP trigger, a schedule trigger, or any other available trigger based on your requirements.

Add an action to trigger the ADF pipeline

  • After adding the trigger, click on "New step" to add an action.
  • Search for "Data Factory" and select the "Create a pipeline run" action.
    Pipeline
  • Create a Connection to Connect Data Factory using OAuth/ServicePrincipal/Managed Identity. Note: Managed Identity is recommended.
    Data Factorty
  • Configure the action by selecting your Data Factory, specifying the pipeline name, and providing the necessary parameters for the pipeline run.
    Pipeline run

Save and test the logic app

  • Save your Logic App.
  • Trigger the Logic App using the configured trigger (e.g., send an HTTP request, wait for the schedule, etc.) or use the Run Option to Trigger.
    Logic App
  • Verify the Logic App Run History.
    Run History
  • Verify that the ADF pipeline is triggered by checking the pipeline runs in your Data Factory. DataFactory -> Launch Studio -> Monitor -> Pipeline Runs.
    Launch Studio

This powerful combination of Logic App and Data Factory services can help you build robust and automated data workflows, improving efficiency and productivity.