In this article, you look at the steps to connect to a semantic model from a PowerTable sheet. Connect to an existing semantic model and create a table app. The data table, along with any changes or updates, is saved to your preferred destination database.
Select your Connection and the required Semantic Model.
Select Next.
Capacity workspaces with Pro licenses are not supported. Make sure to select a workspace of premium capacity.
Map and assign your data to the table by selecting the required fields and corresponding values.
Set the primary key fields by selecting the three dots and selecting primary keys.
If needed, you can use filters to include only specific field values from the table.
Select Next.
Configure columns
PowerTable sheets automatically detect column properties and rename any unsupported column names to match the supported format. You can review and modify them as needed.
Review the detected column settings.
If the primary key is unselected, select the appropriate column as the primary key.
Configure columns as required:
Mark fields as Mandatory if they must contain values.
Set fields with distinct values as Unique Key to allow only unique values.
Set a column as an Identity Column when you need PowerTable to generate unique, sequential values automatically. Only numeric columns and primary key columns can be configured as identity columns.
Modify column properties, including:
Length
Data type
Input type
Display name
Enter default values for columns, if required.
Optionally, select Add Column to create additional columns.
To create a composite primary key, select Unique Combination, and then select two or more columns to define a unique combination key.
Select Finish.
In this step, you can enable Slowly Changing Dimensions (SCDs) by turning the toggle. For a table, this is a one-time configuration that cannot be modified later.
The table app is created successfully in PowerTable with the configured columns and values. Select Save to save your table.
Do changes made in PowerTable update the semantic model or its underlying data source?
No. PowerTable uses the semantic model only to seed the destination table.
When you create a PowerTable app from a semantic model, PowerTable reads the semantic model and copies its data into the selected Fabric SQL database destination table.
After the initial load:
All changes made in PowerTable are written directly to the Fabric SQL database destination table.
Changes made in PowerTable don't update the semantic model or its underlying data source.
Are there any exceptions to this based on the underlying data source (Lakehouse, Warehouse, or external database) or on the semantic model storage mode?
No. The Save to Database option always writes data to the configured Fabric SQL database destination table, regardless of the semantic model's underlying data source or storage mode.
When you create a PowerTable app from a semantic model:
The semantic model is used only to seed the PowerTable with initial data. The underlying data source, whether a Lakehouse, Warehouse, or external database, isn't written to.
The semantic model's storage mode, whether Import, DirectQuery, or Direct Lake, doesn't affect the write path. It only determines how PowerTable reads the seed data.
After the initial load, all changes made in PowerTable are written directly to the configured Fabric SQL database destination table.
When are newly added members in PowerTable reflected on a planning sheet? Is it supported?
Yes. PowerTable supports this scenario. Newly added members appear in the planning sheet in two ways:
Direct connection: When PowerTable writes to a Fabric SQL database table, the table appears in the planning's data pane under Data > From Sheets, where you can assign its columns directly as dimensions and measures. There is no replication or intermediate semantic model. Newly added members are available in the planning sheet as soon as the changes are committed.
Direct Lake connection: When the planning sheet uses a Direct Lake semantic model, PowerTable writes the data to the Fabric SQL database table, which is automatically replicated to OneLake. The Direct Lake semantic model reads the replicated data from OneLake. With automatic updates enabled, newly added members are automatically reflected in the planning sheet after the changes reach OneLake, although a short delay might occur because replication has near-real-time latency.
Schema changes, such as adding or modifying columns, might require a semantic model refresh.
