A pipeline in Azure Data Factory (ADF) is a logical grouping of activities that together perform a task. In this article, we are going to learn the steps to create a ADF pipeline.

• Log into the Azure portal: https://portal.azure.com/
• Open Azure Data Factory Studio and navigate to your Azure Data Factory instance. Once there, open the Azure Data Factory Studio.
  

Create a New Pipeline

• Go to the Author tab: In the ADF Studio, select the “Author” tab on the left-hand side.
  
• Create Pipeline: Click on the “+” button (Create new resource) and select “Pipeline”.
  

Add Activities to the Pipeline

• Choose Activities: In the pipeline canvas, you will see an assortment of activities you can add to your pipeline, such as Copy Data, Data Flow, Execute Pipeline, ForEach Loop, etc.
  
• Drag and Drop Activities: Drag and drop the desired activities onto the canvas.
  
  
• Configure Each Activity: Click on each activity to configure its settings, such as source and sink datasets for a Copy Data activity, or the specific details of a stored procedure for a Stored Procedure activity.

Configure Activities

• Set Properties: For each activity, set the required properties in the settings pane. This might include linking to datasets, linked services, and specifying parameters.
• Connect Activities: If your pipeline has multiple activities, you can connect them to define the order of execution. Drag the green output handle from one activity to the next to create a success link.

Parameterize the Pipeline (Optional)

• Create Parameters: You can create parameters for your pipeline and use these parameters within your activities for dynamic behavior.
• Assign Values: Assign values to these parameters at runtime for flexibility, especially useful when the same pipeline is used for different scenarios or datasets.

Validate and Debug the Pipeline

• Validate Pipeline: Use the Validate button to check for errors or issues.
• Debug: Use the Debug option to test run your pipeline. Debugging allows you to run the pipeline in the ADF UI and see the results without having to publish or trigger the pipeline fully.
  

Publish and Trigger the Pipeline

• Publish: Once your pipeline is ready and tested, click on the “Publish” button to save your pipeline to the Data Factory service.
• Trigger Manually or Schedule: You can manually trigger the pipeline or create a trigger (schedule or event-based) to run it automatically at specified times or in response to certain events.

Best Practices

• Modular Design: Keep your pipelines modular by breaking down complex workflows into smaller, reusable pipelines.
• Error Handling: Implement error handling in your pipelines using activities like the “If Condition” or “Set Variable” to manage and respond to failures.
• Monitoring: Utilize the monitoring features in ADF to track pipeline runs and performance.
• Documentation: Maintain clear documentation for each pipeline, explaining its purpose, design, and any important configurations.