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Quickstart: Create your first pipeline to copy data

In this quickstart, you'll build a data pipeline that moves a sample dataset into a Lakehouse. It's a simple way to see how pipeline copy activities work and how to load data into a Lakehouse.

Tip

You can also use a Copy job to move data from one place to another. Check out this decision guide to help you pick the right tool.

Prerequisites

Before you begin, make sure you have the following setup:

Create a data pipeline

  1. Go to Power BI.

  2. Select the Power BI icon in the lower left, then choose Fabric to open the Microsoft Fabric homepage.

  3. Go to your Microsoft Fabric workspace. If you made a new workspace as a prerequisite, use that one.

    Screenshot of the workspaces window where you navigate to your workspace.

  4. Select New item, pick Data pipeline, and enter a name for your pipeline.

    Screenshot showing the new data pipeline button in the newly created workspace. Screenshot showing the name of creating a new pipeline.

Copy data with your data pipeline

  1. In your data pipeline, select Copy data assistant.

    Screenshot showing the Copy data button.

  2. Choose the Sample data tab at the top of the data source browser page, then select the Public Holidays sample data, and then Next.

    Screenshot showing the Choose data source page of the Copy data assistant with the Public Holidays sample data selected.

  3. On the Connect to data source page of the assistant, the preview for the Public Holidays sample data is displayed, and then select Next.

    Screenshot showing the sample data for the Public Holidays sample data.

  4. To configure your destination, select Lakehouse.

    Screenshot showing the selection of the Lakehouse destination in the Copy data assistant.

  5. Enter a Lakehouse name, then select Create and connect.

    Screenshot showing the specified name for the new Lakehouse.

  6. Configure and map your source data to the destination Lakehouse table. Select Tables for the Root folder and Load to new table for Load settings. Provide a Table name and select Next.

    Screenshot showing the Connect to data destination page of the Copy data assistant with Tables selected and a table name for the sample data provided.

  7. Review your copy activity settings, then select Save + run to finish. You can go back and change any settings if you need to. If you just want to save your pipeline without running it right away, clear the Start data transfer immediately checkbox.

    Screenshot of the Review + create page of the Copy data assistant highlighting source and destination.

  8. The Copy activity now appears in your pipeline. When you select the Copy data activity, you'll see all its settings—including advanced options—in the tabs below the canvas.

    Screenshot showing the completed Copy activity with the Copy activity settings tabs highlighted.

Run and schedule your data pipeline

  1. If you didn't choose to Save + run on the Review + save page of the Copy data assistant, switch to the Home tab and select Run. A confirmation dialog is displayed. Then select Save and run to start the activity.

    Screenshot showing the Run button on the Home tab, and the Save and run prompt displayed.

  2. You can watch your pipeline run and see the results on the Output tab below the canvas. To check the details, select the activity name in the output list.

    Screenshot showing the Output tab of the pipeline run in-progress with the Details button highlighted in the run status.

  3. The run details page shows how much data your pipeline read and wrote, along with other helpful info about the run.

    Screenshot showing the run details window.

  4. You can set your pipeline to run on a schedule. For example, select Schedule to open the scheduling options, then pick how often you want it to run—for example, every 15 minutes.

    Screenshot showing the schedule dialog for the pipeline with a 15-minute recurring schedule.

This quickstart walked you through copying sample data into a Lakehouse using a data pipeline as a simple way to get hands-on with pipelines and see how easy it is to move data.

Next, learn how to monitor your pipeline runs and keep an eye on your data.