Power BI semantic models in Microsoft Fabric

2025-07-22

Applies to: ✅ SQL analytics endpoint, Warehouse, and Mirrored Database in Microsoft Fabric

In Microsoft Fabric, Power BI semantic models are a logical description of an analytical domain, with metrics, business friendly terminology, and representation, to enable deeper analysis. This semantic model is typically a star schema with facts that represent a domain, and dimensions that allow you to analyze, or slice and dice the domain to drill down, filter, and calculate different analyses.

When you create a semantic model on a lakehouse or warehouse, you choose which tables to add. From there, you can manually update a Power BI semantic model.

Note

Starting August 25, 2025, Power BI default semantic models are no longer created automatically when a warehouse, lakehouse, or mirrored item is created. If your item doesn't have a semantic model already, you can create a Power BI semantic model. Existing default semantic models will be converted to regular semantic models. For more information, see Sunsetting Default Semantic Models.

Microsoft has renamed the Power BI dataset content type to Power BI semantic model or just semantic model. This applies to Microsoft Fabric as well. For more information, see New name for Power BI datasets. To learn more about Power BI semantic models, see Semantic models in the Power BI service.

Direct Lake mode

Direct Lake mode is a groundbreaking new engine capability to analyze very large datasets in Power BI. The technology is based on the idea of consuming parquet-formatted files directly from a data lake, without having to query a Warehouse or SQL analytics endpoint, and without having to import or duplicate data into a Power BI semantic model. This native integration brings a unique mode of accessing the data from the Warehouse or SQL analytics endpoint, called Direct Lake. Direct Lake overview has further information about this storage mode for Power BI semantic models.

Direct Lake provides the most performant query and reporting experience. Direct Lake is a fast path to consume the data from the data lake straight into the Power BI engine, ready for analysis.

In traditional DirectQuery mode, the Power BI engine directly queries the data from the source for each query execution, and the query performance depends on the data retrieval speed. DirectQuery eliminates the need to copy data, ensuring that any changes in the source are immediately reflected in query results.
In Import mode, the performance is better because the data is readily available in memory, without having to query the data from the source for each query execution. However, the Power BI engine must first copy the data into the memory, at data refresh time. Any changes to the underlying data source are picked up during the next data refresh.
Direct Lake mode eliminates the Import requirement to copy the data by consuming the data files directly into memory. Because there's no explicit import process, it's possible to pick up any changes at the source as they occur. Direct Lake combines the advantages of DirectQuery and Import mode while avoiding their disadvantages. Direct Lake mode is the ideal choice for analyzing very large datasets and datasets with frequent updates at the source. Direct Lake will automatically fallback to DirectQuery using the SQL analytics endpoint of the Warehouse or SQL analytics endpoint when Direct Lake exceeds limits for the SKU, or uses features not supported, allowing report users to continue uninterrupted.
Direct Lake mode is the storage mode for new Power BI semantic models created on a Warehouse or SQL analytics endpoint.
Using Power BI Desktop, you can also create Power BI semantic models using the SQL analytics endpoint of Warehouse or SQL analytics endpoint as a data source for semantic models in import or DirectQuery storage mode.

Manually update a Power BI semantic model

By default, Fabric does not automatically add tables and views to a Power BI semantic model when created, and there is no automatic sync for semantic models from their warehouse or SQL analytics endpoint source.

Once there are objects in a Power BI semantic model, you can validate or visually inspect the tables included:

Open your Semantic model item in your Fabric workspace.
Switch the mode from Viewing to Editing.
Review the default layout for the semantic model. Make changes as needed. For more information, see Edit data models in the Power BI service.

Monitor a Power BI semantic model

You can monitor and analyze activity on a semantic model with SQL Server Profiler by connecting to the XMLA endpoint.

SQL Server Profiler installs with SQL Server Management Studio (SSMS), and allows tracing and debugging of semantic model events. Although officially deprecated for SQL Server, Profiler is still included in SSMS and remains supported for Analysis Services and Power BI. Use with a Fabric semantic model requires SQL Server Profiler version 18.9 or higher. Users must specify the semantic model as the initial catalog when connecting with the XMLA endpoint. To learn more, see SQL Server Profiler for Analysis Services.

Script a Power BI semantic model

You can script out a Power BI semantic model from the XMLA endpoint with SQL Server Management Studio (SSMS).

View the Tabular Model Scripting Language (TMSL) schema of the semantic model by scripting it out via the Object Explorer in SSMS. To connect, use the semantic model's connection string, which looks like powerbi://api.powerbi.com/v1.0/myorg/username. You can find the connection string for your semantic model in the Settings, under Server settings. From there, you can generate an XMLA script of the semantic model via SSMS's Script context menu action. For more information, see Dataset connectivity with the XMLA endpoint.

Scripting requires Power BI write permissions on the Power BI semantic model. With read permissions, you can see the data but not the schema of the semantic model.

Create a new Power BI semantic model

You can create Power BI semantic models based on lakehouse, SQL analytics endpoint, or Warehouse items in Microsoft Fabric.

Create a new Power BI semantic model in Direct Lake mode

These new Power BI semantic models can be edited in the workspace using Open data model and can be used with other features such as write DAX queries and semantic model row-level security.

To create a Power BI semantic model using Direct Lake mode, follow these steps:

In the Fabric portal, create a new semantic model based on the desired item:
- Open the lakehouse and select New Power BI semantic model from the ribbon.
- Alternatively, open the relevant item, such as your warehouse or SQL analytics endpoint, select New semantic model.
Enter a name for the new semantic model, select a workspace to save it in, and pick the tables to include. Then select Confirm.
The new Power BI semantic model can be edited in the workspace, where you can add relationships, measures, rename tables and columns, choose how values are displayed in report visuals, and much more. If the model view does not show after creation, check the pop-up blocker of your browser.
To edit the Power BI semantic model later, select Open data model from the semantic model context menu or item details page to edit the semantic model further.

Power BI reports can be created in the workspace by selecting New report from web modeling, or in Power BI Desktop by live connecting to this new semantic model. To learn more on how to connect to semantic models in the Power BI service from Power BI Desktop

Create a new Power BI semantic model in import or DirectQuery storage mode

Having your data in Microsoft Fabric means you can create Power BI semantic models in any storage mode -- Direct Lake, import, or DirectQuery. You can create additional Power BI semantic models in import or DirectQuery mode using SQL analytics endpoint or Warehouse data.

To create a Power BI semantic model in import or DirectQuery mode, follow these steps:

Open Power BI Desktop, sign in, and select OneLake.
Choose the SQL analytics endpoint of the lakehouse or warehouse.
Select the Connect button dropdown list and choose Connect to SQL endpoint.
Select import or DirectQuery storage mode and the tables to add to the semantic model.

From there you can create the Power BI semantic model and report to publish to the workspace when ready.

Create a new, blank Power BI semantic model

The New Power BI semantic model button creates a new blank semantic model.

Limitations

Semantic models in Fabric follow the current limitations for semantic models in Power BI. Learn more:
- Azure Analysis Services resource and object limits
- Data types in Power BI Desktop - Power BI
If the parquet, Apache Spark, or SQL data types can't be mapped to one of the Power BI desktop data types, they are dropped as part of the sync process. This is in line with current Power BI behavior. For these columns, we recommend that you add explicit type conversions in their ETL processes to convert it to a type that is supported. If there are data types that are needed upstream, users can optionally specify a view in SQL with the explicit type conversion desired. This will be picked up by the sync or can be added manually as previously indicated.
Semantic models can only be edited in the SQL analytics endpoint or warehouse.
Starting August 25, 2025, Power BI default semantic models are no longer created automatically when a warehouse, lakehouse, or mirrored item is created. If your item doesn't have a semantic model already, you can create a Power BI semantic model. Existing default semantic models will be converted to regular semantic models. For more information, see Sunsetting Default Semantic Models.

Semantic models in the Power BI service