Microsoft Fabric Lakehouse And How To Engineer Data Into The Lakehouse


In this article, we will learn about the Microsoft Fabric Lakehouse object located in the Synapse Data Engerring platform. The Microsoft Fabric Lakehouse is a versatile data architecture platform designed to centralize the storage, administration, and examination of both structured and unstructured data in one unified repository. This adaptable and expandable solution empowers enterprises to effectively manage extensive data sets, utilizing a variety of tools and frameworks for data processing and analysis. It seamlessly incorporates other data management and analytics tools, delivering a comprehensive solution for data engineering and analytics needs.

Interacting with Lakehouse Components

  • The Lakehouse Explorer: Serving as the primary interface for the Lakehouse, the explorer enables users to load data into their Lakehouse, explore data within it using the object explorer, and manage tasks such as setting MIP labels and more. To delve deeper into the explorer experience, navigate through the Fabric Lakehouse Explorer.
  • Notebooks: Users can utilize notebooks to write code for tasks like reading, transforming, and writing data directly into the Lakehouse as tables or folders. Gain a better understanding of using notebooks for your Lakehouse by exploring these resources: "Exploring Data in Your Lakehouse with a Notebook" and "How to Utilize a Notebook to Load Data into Your Lakehouse."
  • Pipelines: Data engineers have the option to employ data integration tools, such as the pipeline copy tool, to extract data from various sources and deposit it into the Lakehouse. To discover more about the copy activity, refer to "How to Copy Data Using Copy Activity."
  • Apache Spark Job Definitions: Data engineers can craft robust applications and oversee the execution of compiled Spark jobs in languages like Java, Scala, and Python. Explore further insights into Spark jobs by reading "What Is an Apache Spark Job Definition?"
  • Dataflows Gen 2: Data engineers can leverage Dataflows Gen 2 to ingest and prepare their data. For comprehensive instructions on loading data using dataflows, consult "Create Your First Dataflow to Obtain and Transform Data."

Creating a Workspace

To use the Synapse Data Engineering platform of the Fabric in creating a Lakehouse, it is required to create a Workspace. To create the workspace.

  • Click on the PowerBI icon at the bottom left of the Microsoft Fabric welcome page and select Workspaces.
    creating lakehouse
    In the workspaces pane, click on New Workspace.
  • Next, provide a suitable name for the workspace. For this article, TransactionLakehouse is provided as the name.
    transaction lakehouse
  • Click on Apply.

Switch to Synapse Data Engineering Platform and Create Lakehouse

  • After creating the workspace, at the bottom left of the screen, click on the PowerBI icon and select Data Engineering.
    Engeering platform
  • To create the lakehouse, click on Lakehouse and provide a name for the lakehouse Dataset is used as the lakehouse name in this article.
    new lakehouse
  • Click Create.
    The screenshot below shows the created lakehouse, including the explorer and different ways to pull data from DataFlow Gen2, Data Pipeline, and Notebook. At this stage, we have not ingested data. click create lakehouse

Get Data From Excel

Next, we want to ingest data using the DataFlow Gen 2, which is also known as Power Query Online. The datasets to be ingested are stored in an Excel file with many dimension tables and one fact table.

  • Click on New Dataflow Gen2.
    new dataflow gen2
  • In the Power Query online editor, click on Import from Excel. In the Connection Settings, we can either connect to a link to a file on OneDrive or Upload an Excel workbook. For this article, we are going to upload an Excel workbook.
  • Click on Upload File and select the workbook.
    connect data source
  • Click on Next. The tables are now loaded into the Power Query online, as seen in the screenshot below.
    choose data
    The dataset does not require the need for transformation. Hence, we will proceed to load into the Lakehouse.
  • At the bottom right, click on Create. From the screenshot below, we can see all the steps we have gone through and the deployment of the data into the Lakehouse through Dataflow Gen 2. Note you might need to wait for minutes depending on the size of the dataset.
    To access the tables in the Lakehouse, click on the Dataset name containing the Lakehouse icon and Lakehouse type.
  • There we go! We can see all the tables in the Lakehouse Explorer.
    lakehouse explorer