Analyze data in a data warehouse

In Microsoft Fabric, a data warehouse provides a relational database for large-scale analytics. Unlike the default read-only SQL endpoint for tables defined in a lakehouse, a data warehouse provides full SQL semantics; including the ability to insert, update, and delete data in the tables.

This lab will take approximately 30 minutes to complete.

Note: You need a Microsoft Fabric trial to complete this exercise.

Create a workspace

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

  1. On the Microsoft Fabric home page at https://app.fabric.microsoft.com/home?experience=fabric, select Data Warehouse.
  2. In the menu bar on the left, select Workspaces (the icon looks similar to đź—‡).
  3. Create a new workspace with a name of your choice, selecting a licensing mode that includes Fabric capacity (Trial, Premium, or Fabric).

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

    Screenshot of an empty workspace in Fabric.

Create a data warehouse

Now that you have a workspace, it’s time to create a data warehouse. The Synapse Data Warehouse home page includes a shortcut to create a new warehouse:

  1. In the Data Warehouse home page, create a new Warehouse with a name of your choice.

    After a minute or so, a new warehouse will be created:

    Screenshot of a new warehouse.

Create tables and insert data

A warehouse is a relational database in which you can define tables and other objects.

  1. In your new warehouse, select the T-SQL tile, and use the following CREATE TABLE statement:

    CREATE TABLE dbo.DimProduct
(
    ProductKey INTEGER NOT NULL,
    ProductAltKey VARCHAR(25) NULL,
    ProductName VARCHAR(50) NOT NULL,
    Category VARCHAR(50) NULL,
    ListPrice DECIMAL(5,2) NULL
);
GO
  2. Use the â–· Run button to run the SQL script, which creates a new table named DimProduct in the dbo schema of the data warehouse.
  3. Use the Refresh button on the toolbar to refresh the view. Then, in the Explorer pane, expand Schemas > dbo > Tables and verify that the DimProduct table has been created.

  4. On the Home menu tab, use the New SQL Query button to create a new query, and enter the following INSERT statement:

    INSERT INTO dbo.DimProduct
VALUES
(1, 'RING1', 'Bicycle bell', 'Accessories', 5.99),
(2, 'BRITE1', 'Front light', 'Accessories', 15.49),
(3, 'BRITE2', 'Rear light', 'Accessories', 15.49);
GO
  5. Run the new query to insert three rows into the DimProduct table.
  6. When the query has finished, in the Explorer pane, select the DimProduct table and verify that the three rows have been added to the table.
  7. On the Home menu tab, use the New SQL Query button to create a new query. Then copy and paste the Transact-SQL code from https://raw.githubusercontent.com/MicrosoftLearning/dp-data/main/create-dw.txt into the new query pane.
  8. Run the query, which creates a simple data warehouse schema and loads some data. The script should take around 30 seconds to run.
  9. Use the Refresh button on the toolbar to refresh the view. Then in the Explorer pane, verify that the dbo schema in the data warehouse now contains the following four tables:
    • DimCustomer
    • DimDate
    • DimProduct
    • FactSalesOrder

    Tip: If the schema takes a while to load, just refresh the browser page.

Define a data model

A relational data warehouse typically consists of fact and dimension tables. The fact tables contain numeric measures you can aggregate to analyze business performance (for example, sales revenue), and the dimension tables contain attributes of the entities by which you can aggregate the data (for example, product, customer, or time). In a Microsoft Fabric data warehouse, you can use these keys to define a data model that encapsulates the relationships between the tables.

  1. In the toolbar, select the Model layouts button.

  2. In the model pane, rearrange the tables in your data warehouse so that the FactSalesOrder table is in the middle, like this:

    Screenshot of the data warehouse model page.

Note: The views frequently_run_queries, long_running_queries, exec_sessions_history, and exec_requests_history are part of the queryinsights schema automatically created by Fabric. It is a feature that provides a holistic view of historical query activity on the SQL analytics endpoint. Since this feature is out of the scope of this exercise, those views should be ignored for now.

  1. Drag the ProductKey field from the FactSalesOrder table and drop it on the ProductKey field in the DimProduct table. Then confirm the following relationship details:
    • From table: FactSalesOrder
    • Column: ProductKey
    • To table: DimProduct
    • Column: ProductKey
    • Cardinality: Many to one (*:1)
    • Cross filter direction: Single
    • Make this relationship active: Selected
    • Assume referential integrity: Unselected
  2. Repeat the process to create many-to-one relationships between the following tables:
    • FactSalesOrder.CustomerKey → DimCustomer.CustomerKey
    • FactSalesOrder.SalesOrderDateKey → DimDate.DateKey

    When all of the relationships have been defined, the model should look like this:

    Screenshot of the model with relationships.

Query data warehouse tables

Since the data warehouse is a relational database, you can use SQL to query its tables.

Query fact and dimension tables

Most queries in a relational data warehouse involve aggregating and grouping data (using aggregate functions and GROUP BY clauses) across related tables (using JOIN clauses).

  1. Create a new SQL Query, and run the following code:

    SELECT  d.[Year] AS CalendarYear,
         d.[Month] AS MonthOfYear,
         d.MonthName AS MonthName,
        SUM(so.SalesTotal) AS SalesRevenue
FROM FactSalesOrder AS so
JOIN DimDate AS d ON so.SalesOrderDateKey = d.DateKey
GROUP BY d.[Year], d.[Month], d.MonthName
ORDER BY CalendarYear, MonthOfYear;

    Note that the attributes in the date dimension enable you to aggregate the measures in the fact table at multiple hierarchical levels - in this case, year and month. This is a common pattern in data warehouses.

  2. Modify the query as follows to add a second dimension to the aggregation.

    SELECT  d.[Year] AS CalendarYear,
        d.[Month] AS MonthOfYear,
        d.MonthName AS MonthName,
        c.CountryRegion AS SalesRegion,
       SUM(so.SalesTotal) AS SalesRevenue
FROM FactSalesOrder AS so
JOIN DimDate AS d ON so.SalesOrderDateKey = d.DateKey
JOIN DimCustomer AS c ON so.CustomerKey = c.CustomerKey
GROUP BY d.[Year], d.[Month], d.MonthName, c.CountryRegion
ORDER BY CalendarYear, MonthOfYear, SalesRegion;

  3. Run the modified query and review the results, which now include sales revenue aggregated by year, month, and sales region.

Create a view

A data warehouse in Microsoft Fabric has many of the same capabilities you may be used to in relational databases. For example, you can create database objects like views and stored procedures to encapsulate SQL logic.

  1. Modify the query you created previously as follows to create a view (note that you need to remove the ORDER BY clause to create a view).

    CREATE VIEW vSalesByRegion
AS
SELECT  d.[Year] AS CalendarYear,
        d.[Month] AS MonthOfYear,
        d.MonthName AS MonthName,
        c.CountryRegion AS SalesRegion,
       SUM(so.SalesTotal) AS SalesRevenue
FROM FactSalesOrder AS so
JOIN DimDate AS d ON so.SalesOrderDateKey = d.DateKey
JOIN DimCustomer AS c ON so.CustomerKey = c.CustomerKey
GROUP BY d.[Year], d.[Month], d.MonthName, c.CountryRegion;
  2. Run the query to create the view. Then refresh the data warehouse schema and verify that the new view is listed in the Explorer pane.

  3. Create a new SQL query and run the following SELECT statement:

    SELECT CalendarYear, MonthName, SalesRegion, SalesRevenue
FROM vSalesByRegion
ORDER BY CalendarYear, MonthOfYear, SalesRegion;

Create a visual query

Instead of writing SQL code, you can use the graphical query designer to query the tables in your data warehouse. This experience is similar to Power Query online, where you can create data transformation steps with no code. For more complex tasks, you can use Power Query’s M (Mashup) language.

  1. On the Home menu, expand the options under New SQL query and select New visual query.

  2. Drag FactSalesOrder onto the canvas. Notice that a preview of the table is displayed in the Preview pane below.

  3. Drag DimProduct onto the canvas. We now have two tables in our query.

  4. Use the (+) button on the FactSalesOrder table on the canvas to Merge queries. Screenshot of the canvas with the FactSalesOrder table selected.

  5. In the Merge queries window, select DimProduct as the right table for merge. Select ProductKey in both queries, leave the default Left outer join type, and click OK.

  6. In the Preview, note that the new DimProduct column has been added to the FactSalesOrder table. Expand the column by clicking the arrow to the right of the column name. Select ProductName and click OK.

    Screenshot of the preview pane with the DimProduct column expanded, with ProductName selected.

  7. If you’re interested in looking at data for a single product, per a manager request, you can now use the ProductName column to filter the data in the query. Filter the ProductName column to look at Cable Lock data only.

  8. From here, you can analyze the results of this single query by selecting Visualize results or Open in Excel. You can now see exactly what the manager was asking for, so we don’t need to analyze the results further.

Visualize your data

You can easily visualize the data in either a single query, or in your data warehouse. Before you visualize, hide columns and/or tables that aren’t friendly to report designers.

  1. Select the Model layouts button.

  2. Hide the following columns in your Fact and Dimension tables that are not necessary to create a report. Note that this does not remove the columns from the model, it simply hides them from view on the report canvas.
    1. FactSalesOrder
      • SalesOrderDateKey
      • CustomerKey
      • ProductKey
    2. DimCustomer
      • CustomerKey
      • CustomerAltKey
    3. DimDate
      • DateKey
      • DateAltKey
    4. DimProduct
      • ProductKey
      • ProductAltKey

  3. Now you’re ready to build a report and make this dataset available to others. On the Reporting menu, select New report. This will open a new window, where you can create a Power BI report.

  4. In the Data pane, expand FactSalesOrder. Note that the columns you hid are no longer visible.

  5. Select SalesTotal. This will add the column to the Report canvas. Because the column is a numeric value, the default visual is a column chart.
  6. Ensure that the column chart on the canvas is active (with a gray border and handles), and then select Category from the DimProduct table to add a category to your column chart.

  7. In the Visualizations pane, change the chart type from a column chart to a clustered bar chart. Then resize the chart as necessary to ensure that the categories are readable.

    Screenshot of the Visualizations pane with the bar chart selected.

  8. In the Visualizations pane, select the Format your visual tab and in the General sub-tab, in the Title section, change the Text to Total Sales by Category.

  9. In the File menu, select Save. Then save the report as Sales Report in the workspace you created previously.

  10. In the menu hub on the left, navigate back to the workspace. Notice that you now have three items saved in your workspace: your data warehouse, its default dataset, and the report you created.

    Screenshot of the workspace with the three items listed.

Clean up resources

In this exercise, you have created a data warehouse that contains multiple tables. You used SQL to insert data into the tables and query them. and also used the visual query tool. Finally, you enhanced the data model for the data warehouse’s default dataset and used it as the source for a report.

If you’ve finished exploring your data warehouse, you can delete the workspace you created for this exercise.

  1. In the bar on the left, select the icon for your workspace to view all of the items it contains.
  2. In the … menu on the toolbar, select Workspace settings.
  3. In the General section, select Remove this workspace.