Explore data analytics in Microsoft Fabric

In this lab, you’ll explore how to bring in and analyze data using Microsoft Fabric, an all-in-one analytics platform that handles everything from storing data to building reports in one place.

You’ll work with a lakehouse, which is a single place that can store both raw data files and organized tables you can query with SQL. You’ll then use a copy job (an automated, guided way to copy data from one place to another) to bring in a real-world set of New York City taxi trips, and finish by running SQL queries to answer questions about the data.

By completing this lab, you will:

  • Understand Microsoft Fabric lakehouse concepts: Learn how to create workspaces and lakehouses, which are central to organizing and managing data in Fabric.
  • Bring in data using a copy job: Use a guided copy job to load external data into the lakehouse, making it ready to query without writing any code.
  • Explore and query data with SQL: Analyze the loaded data using familiar SQL queries, gaining insights directly within Fabric.
  • Manage resources: Learn good habits for cleaning up resources to avoid unnecessary charges.

Don’t worry if these terms are new, every step is explained as you go.

Background on the NYC Taxi dataset

The “NYC Taxi - Green” dataset contains detailed records of taxi trips in New York City, including pickup and drop-off times, locations, trip distances, fares, and passenger counts. It’s widely used in data analytics for exploring how people move around a city, forecasting demand, and spotting unusual patterns. In this lab, you’ll use this real-world dataset to practice bringing in and analyzing data in Microsoft Fabric.

This lab will take approximately 30 minutes to complete.

Note: You’ll need a Microsoft Fabric license to complete this exercise. See Getting started with Fabric for details of how to enable a free Fabric trial license. You will need a Microsoft school or work account to do this. If you don’t have one, you can sign up for a trial of Microsoft Office 365 E3 or higher.

The first time you use any Microsoft Fabric features, prompts with tips may appear. Dismiss these.

Create a workspace

Before working with data in Fabric, create a workspace with the Fabric trial enabled.

What is a workspace? Think of it as a project folder that holds everything you create in Fabric (lakehouses, pipelines, reports, and more). Enabling Fabric capacity gives the workspace the computing power needed to run those items.

  1. Navigate to the Microsoft Fabric home page at https://app.fabric.microsoft.com/home?experience=fabric in a browser, and sign in with your Fabric credentials.

  2. At the bottom of the menu bar on the left is an experience switcher. If it shows Power BI, select it and choose Fabric so that all of the data engineering features used in this lab are available.

    Screenshot of the experience switcher showing the Fabric and Power BI options.

  3. In the menu bar on the left, select Workspaces (the icon looks similar to 🗇).

    Screenshot of the Workspaces flyout with the New workspace button.

  4. Select + New workspace, give your workspace a name (such as dp900-fabric-lakehouse), and in the Advanced section select a licensing mode that includes Fabric capacity (Trial, Premium, or Fabric). Then select Apply.

    Tip Selecting a capacity that includes Fabric gives the workspace the engines needed for data engineering tasks. Using a dedicated workspace keeps your lab resources isolated and easy to clean up.

    Screenshot of the Create a workspace pane with a name and licensing mode.

  5. When your new workspace opens, it should be empty.

    Screenshot of an empty workspace in Fabric.

Create a lakehouse

Now that you have a workspace, it’s time to create a lakehouse for your data files.

What is a lakehouse? It’s a single storage area that holds both raw data files *and* organized tables. The tables can be queried with SQL, just like in a regular database, while the files area can hold data in any format. This combination is why it’s called a “lake” (flexible file storage) plus a “house” (structured tables).

  1. In your workspace toolbar, select + New item. In the New item pane, search for Lakehouse and select the Lakehouse tile.

    Screenshot of the New item pane filtered to show the Lakehouse tile.

  2. In the New Lakehouse dialog, enter a name such as taxi_lakehouse, leave Lakehouse schemas selected, and select Create.

    Screenshot of the New Lakehouse dialog with a name and the Lakehouse schemas option.

    After a minute or so, a new lakehouse will be created.

  3. View the new lakehouse, and note that the Explorer pane on the left enables you to browse tables and files in the lakehouse:

    • The Tables folder contains tables that you can query using SQL semantics, organized into schemas (such as the default dbo schema). Tables in a Microsoft Fabric lakehouse are based on the open source Delta Lake file format, commonly used in Apache Spark.
    • The Files folder contains data files in the OneLake storage for the lakehouse that aren’t associated with managed delta tables. You can also create shortcuts in this folder to reference data that is stored externally.

    Currently, there are no tables or files in the lakehouse.

    Screenshot of a new lakehouse with the Explorer pane and Get data options.

    Tip: Use Files for raw or staged data, and Tables for curated, query-ready datasets. Tables are backed by Delta Lake so they support reliable updates and efficient queries.

Ingest data

A simple way to bring data into the lakehouse is to use a Copy job. This copies the data from a source (in this case, a sample dataset) into a table in your lakehouse.

What is a copy job? It’s a guided, repeatable way to move data from a source into your lakehouse without writing any code. A copy job can run once or on a schedule, which makes it much easier than copying data by hand.

  1. On the Home page for your lakehouse, in the Get data menu, select New copy job.

    Screenshot of the Get data menu with New copy job selected.

  2. In the New Copy job dialog, name the job Ingest Data and select Create.

    Screenshot of the New Copy job dialog with the name Ingest Data.

  3. On the Choose data source page, select the Sample data tab at the top.

    Screenshot of the Choose data source page in the copy job wizard.

  4. Select the NYC Taxi - Green sample dataset.

    Screenshot of the Sample data tab with the NYC Taxi - Green dataset.

  5. On the Choose data page, review the preview of the taxi trip data, and then select Next.

    Screenshot of the data preview for the NYC Taxi - Green dataset.

  6. On the Settings page, ensure Full copy is selected as the read method and Tables is selected as the destination root folder, and then select Next.

    Why these choices? Full copy loads all of the data in a single run, and choosing Tables as the root folder loads the data straight into a managed Delta table that you can query right away.

    Screenshot of the copy job Settings page with Full copy and Tables selected.

  7. On the Map to destination page, set the destination schema to dbo and the destination table name to taxi_rides, and then select Next.

    Screenshot of the Map to destination page with the dbo schema and taxi_rides table.

  8. On the Review + save page, make sure Start data transfer immediately is selected and the Run once option is selected, and then select Save + Run.

    Tip: Starting immediately lets you watch the copy job run and confirm the data arrives without extra steps.

    Screenshot of the copy job Review + save page.

  9. The copy job runs. In the Results pane, wait until the Status shows Succeeded and Tables completed shows 1/1.

    Note: Because the sample data is copied as Parquet files, the Rows read and Rows written counters may show 0 even though the table is created and populated successfully.

    Screenshot of the copy job with a Succeeded status in the Results pane.

  10. In the menu bar at the top, select your lakehouse (you can also find it from your workspace). In the Explorer pane, expand Tables > dbo and select the taxi_rides table to view its contents.

    Tip: If the table doesn’t appear right away, use the … menu on the Tables node and select Refresh.

    Screenshot of the taxi_rides table contents in the lakehouse.

Query data in a lakehouse

Now that you have ingested data into a table in the lakehouse, you can use SQL to query it.

Tip: Lakehouse tables are SQL-friendly. You can analyze data right away without moving it to another system.

  1. At the top right of the lakehouse page, select Analyze data with, and then select SQL analytics endpoint.

    Tip: The SQL analytics endpoint is optimized for running SQL queries over your lakehouse tables and integrates with familiar query tools.

    Screenshot of the Analyze data with menu showing the SQL analytics endpoint option.

  2. In the toolbar, select New SQL query. Then enter the following SQL code into the query editor:

     SELECT  DATENAME(dw,lpepPickupDatetime) AS Day,
         AVG(tripDistance) As AvgDistance
 FROM taxi_rides 
 GROUP BY DATENAME(dw,lpepPickupDatetime)

  3. Select the ▷ Run button to run the query and review the results, which should include the average trip distance for each day of the week.

    Tip: This query groups trips by day name and calculates the average distance, showing a simple example of aggregation you can build on.

    Screenshot of the SQL query and its results showing average distance by day.

Clean up resources

If you’ve finished exploring Microsoft Fabric, you can delete the workspace you created for this exercise.

Tip: Deleting the workspace removes all items created in the lab and helps prevent ongoing charges.

  1. In the bar on the left, select the icon for your workspace to view all of the items it contains.

  2. In the toolbar, select Workspace settings.

  3. In the General section, select Remove this workspace.