The Pivot Table element allows you to dynamically summarize your data and analyze it by category. Users can add different metrics to the table using drag-and-drop and generate summary reports.

Areas of Use

• Sales reporting,
• Customer behavior analysis,
• Performance measurements,
• Financial reporting, etc.

The Pivot Table element only supports web applications.

Restaurant Invoice Analysis Scenario

In an application created for a restaurant chain, the user analyzes customer invoices with Pivot Table.

• Invoices are grouped by gender (Male/Female), day of the week, meal, and party size.
• Tip amounts are calculated as total, average, or percentage.
• This allows the user to easily see which days and which groups received more tips.

Connecting the Data Source

• Go to the Datasources module.
• Click the + icon next to the Tables heading and name the table RestaurantBills.
• Create the following table.

• Then click SQL Actions and name the action RestaurantBills. You can also create it with C#.
• Enter the following SQL command.

‍SELECT ‘Female’ AS PayerGender, ‘Thursday’ AS DayOfWeek, ‘Lunch’ AS Meal, 2 AS PartySize, 45.00 AS TotalBill, 8.00 AS Tip, ‘No’ AS PayerSmokerUNION ALLSELECT ‘Male’, ‘Friday’, ‘Dinner’, 3, 120.00, 20.00, ‘Yes’UNION ALLSELECT ‘Female’, ‘Sunday’, ‘Dinner’, 4, 200.00, 35.00, ‘No’UNION ALLSELECT ‘Male’, ‘Saturday’, ‘Lunch’, 2, 80.00, 12.00, ‘No’;

UI Design Module operations

• Click the Add Action button on the right side of the application screen.
• Then select Custom > RestaurantBills.

Using Properties in the Scenario Context

• Datasource
  
  • Description: The source of the data to be displayed in the Pivot Table.
  • Usage: RestaurantBills.

Using Actions in the Scenario Context

• Export Table Config
  
  • Description: Exports the user's table configuration.
  • Usage: Can be saved as JSON and reloaded.
• Import Table Configs
  
  • Description: Imports a previously saved configuration.
  • Usage: The user can reload their personal templates.

When the Scenario Step is Complete

• Gender and day appear in the table rows, and meal and number of people appear in the columns.
• The total type values are displayed in the cells.
• The user analyzes the data using either the table or the graph view.
• It is easy to see on which days more types were recorded.

Limitations

• Pivot Table only works in web applications.
• The initial loading time may be long for very large data sets.
• Complex pivot structures may be difficult to read on mobile screens.

Tips and Best Practices

• Always use meaningful dimension–measure pairings (e.g., Day → Row, Total Amount → Value).
• Save templates and offer users the option to reload them.
• Activate the chart view to visually support table data.
• Perform pre-filtering in the data source for better performance.