Load Data to Warehouse Table from ADLS Gen2 Using Pipeline | Microsoft Fabric Tutorial

Load Data to Warehouse Table from ADLS Gen2 Using Pipeline

In this step-by-step Microsoft Fabric tutorial, you'll learn how to build a pipeline that connects to Azure Data Lake Storage Gen2, retrieves CSV/Parquet files, maps the data, and loads it into a Fabric Warehouse table. Pipelines in Microsoft Fabric offer a low-code, efficient approach to manage data flows across cloud environments.

✅ How to Configure a Pipeline in Microsoft Fabric

Begin by navigating to your Microsoft Fabric workspace and selecting “New > Data pipeline”. Give your pipeline a meaningful name. You’ll see a blank canvas where you can add different activities like source, transformation, and sink (destination).

Pipelines in Fabric resemble Azure Data Factory and provide native support for integrating data from a wide variety of sources including ADLS Gen2, SQL, Lakehouse, REST APIs, and more.

✅ How to Connect to ADLS Gen2 and Select Source Files

Drag the Copy Data activity onto the canvas. In the source tab:

• Click “+ New” to create a new connection to your ADLS Gen2 account.
• Provide the storage account URL or browse the linked services.
• Navigate to the desired container and folder where your files are stored (e.g. /input/customer.csv).
• Choose file format (CSV, Parquet, etc.), and configure schema detection options.

✅ How to Map and Load Data into Warehouse Tables

On the Sink tab of the Copy Data activity:

• Select your destination as a Microsoft Fabric Warehouse.
• Pick the appropriate Warehouse and table name (e.g., dbo.Customer).
• Enable schema mapping. Fabric attempts auto-mapping, but you can also manually map source columns to destination fields.
• Choose write behavior – e.g., Insert, Upsert, or Truncate + Load.

✅ End-to-End Data Flow Setup and Execution

Once both source and sink are configured:

1. Validate the pipeline to catch schema or connection errors.
2. Click “Publish All” to save your work.
3. Trigger the pipeline manually or schedule it via the trigger tab.

The data will flow from ADLS Gen2 into your Fabric Warehouse, and you can verify it by querying the target table.

✅ Best Practices for Pipeline-Based Data Ingestion

• Use parameterized pipelines to make reusable components for different file sources or tables.
• Monitor execution logs to diagnose failures or slow performance.
• Partition large datasets when reading from lake to avoid memory pressure during ingestion.
• Schedule during off-peak hours to maximize performance and reduce contention.
• Set up retry policies for fault tolerance in case of transient connectivity issues.

🎬 Watch the Full Tutorial

Blog post written with the help of ChatGPT.