Microsoft Fabric - What are Dynamic Pipelines?

Unlock the power of dynamic pipelines in Microsoft Fabric! In this tutorial, we'll explore how to create a flexible data integration pipeline using parameterization, perfect for handling multiple tables in a single workflow.

📌 What Are Dynamic Pipelines?

Dynamic pipelines in Microsoft Fabric use parameters to adjust how data moves through your ETL process. By creating a parent-child pipeline structure, you can simplify your data movement strategy, reducing the need for multiple pipelines for different tables or datasets.

🎯 Benefits of Using Dynamic Pipelines:

• ✅ Automate data movement across multiple tables.
• ✅ Reduce pipeline maintenance with reusable components.
• ✅ Enhance scalability and flexibility in your data factory processes.

📖 Step 1: Creating the Child Pipeline

The child pipeline is where you'll define the data movement task. Follow these steps:

1. Create a new data pipeline in your workspace.
2. Add a Copy Activity and configure the source (Azure SQL Database in this example).
3. Create a Pipeline Parameter called tableName (string type).
4. Use the parameter in the source and destination table names.
5. Ensure the schema mappings are removed for dynamic table movement.

📖 Step 2: Setting Up the Parent Pipeline

The parent pipeline will control the child pipeline and handle multiple tables using a loop:

1. Create a new pipeline and add a Lookup Activity.
2. Use a SQL query to fetch a list of table names.
3. Uncheck the First Row Only option to return a list.
4. Add a For Each Loop to iterate through the list.
5. Within the loop, add the Invoke Pipeline (Legacy) activity.
6. Pass the tableName parameter to the child pipeline using dynamic content.

✅ Best Practices for Dynamic Pipelines:

• 🔑 Use schema flexibility when working with multiple datasets.
• 🔧 Test the pipeline with a smaller dataset before scaling up.
• 📊 Consider adding logging and error-handling steps for better monitoring.

🎯 Why Use Dynamic Pipelines?

Dynamic pipelines reduce the need for manual replication of data movement tasks across multiple datasets. They simplify maintenance and enhance scalability for large data projects.

📌 Final Thoughts

By following this guide, you can create a flexible and scalable ETL pipeline within Microsoft Fabric. Want to learn more? Explore our Microsoft Fabric Bootcamps and master dynamic data integration!

Don't forget to check out the Pragmatic Works' on-demand learning platform for more insightful content and training sessions on Microsoft Fabric and other Microsoft applications. Be sure to subscribe to the Pragmatic Works YouTube channel to stay up-to-date on the latest tips and tricks.