fabric-tmdl-api

Create a Fabric semantic model definition by using the `createItemWithDefinition` API to submit your TMDL content, which automates the initial setup and deployment of the model structure.

Fabric Semantic Model API updates fail when modifying TMDL if you do not include all parts of the definition during the update operation. You must bundle the entire model structure to ensure compatibility and prevent partial updates.

Yes, the Fabric Semantic Model Definition API requires base64 encoding for TMDL content. You must encode your TMDL scripts into base64 format before sending them through the API to ensure successful processing and deployment.

Configure Direct Lake partitions in a Fabric semantic model by following specific API configuration requirements during model creation or update. This ensures your semantic model directly queries the underlying lakehouse data for optimal performance.

Yes, you can automate CRUD operations for Fabric semantic models using the Fabric API. This covers creating, reading, updating, and deleting semantic model definitions to streamline management and ensure compatibility across your Fabric environment.

The best way to handle TMDL updates for Direct Lake semantic models is to include all definition parts in the API request and verify Direct Lake partition configuration requirements. This maintains optimal performance and prevents update failures.