Data binding in ontology (preview)

Data binding in ontology (preview) connects the schema of entity types, relationship types, and properties to concrete data sources that drive enterprise operations and analytics.

By using data binding, you can:

• Integrate data into a semantic layer without copying source data
• Enrich entity types with up-to-date contextual information from batch and real-time sources
• Provide a semantic backbone for AI agents and automation to support reasoning, decision-making, and actions across the enterprise

Prerequisites

Before binding data to your ontology, make sure you have the following prerequisites:

• A Fabric workspace with a Microsoft Fabric-enabled capacity.
• Ontology item (preview) enabled on your Fabric tenant.
• An ontology (preview) item with entity types created.
• Data that you prepared according to these guidelines:
  • The data is organized, and has gone through any necessary ETL required by your business.
  • The data contains all required information for it to be modeled. For more information, see Core concept: Data binding.
  • The data is in Microsoft Fabric—static data in OneLake, time series data in OneLake or an eventhouse.
  • Time series data is in columnar format, meaning it's represented in a table with a row for each timestamped observation. Columns contain time stamps and property values (like temperature or pressure).
  • Lakehouse tables conform to ontology (preview)'s data binding limitations: They are managed, do not have OneLake security enabled, and do not have column mapping enabled.

Key concepts

Data binding uses the following ontology (preview) concepts. For definitions of these terms, see the Ontology (preview) glossary.

• Entity type
• Entity type key
• Entity instance
• Property

Add static data

First, bind static data to entity types in your ontology (preview) item. Create static data bindings before creating time series data bindings.

1. Select the entity to which you want to bind data in the Entity Types pane. This selection opens the Entity type configuration pane for the entity type. In the Bindings tab, select Add data to entity type.

   

2. The data source selection appears. Select a OneLake data source that contains the data to be bound to the entity. Select Connect.

   When the data source loads, choose a specific table from the data source to use as the data binding source table. Select Next.

   

3. For the Binding type, select Static. Select only one type per data binding. An example of static data is a table with descriptive attributes about stores, like the store ID value, square footage, and location.

4. Under Bind your properties, select the source columns from the source table that you want to model on your entity type. Then, enter a name for each property that shows on the entity type. The name can be the same as the source column name or something different.

   Custom property names must be 1–26 characters, contain only alphanumeric characters, hyphens, and underscores, and start and end with an alphanumeric character. Property names must be unique across all entity types.

   

   If you already created properties on your entity type, you can select their names in the Property name column to map data to them. When you select an existing property name, the Source column options are grouped into two sections: Available and Unavailable. Available columns are columns in your source table that match the declared data type of the property you're trying to match. Unavailable columns are ones that don't match the type, so they can't be bound to that property.

   

5. Select Save to save your static data binding.

6. Verify the bindings by viewing a summary of your data bindings in the Bindings tab, and a summary of properties (including properties added during data binding) in the Properties tab.

   

   

7. Next, set the Key. The entity type key value represents a unique identifier for each record of ingested data.

   String and integer columns from your source data are available to select as the entity type key. Together, the columns you select uniquely identify a record.

   

   This process is done once for each entity type.

8. Optionally, select a property modeled on your entity type to use as the Instance display name. This step provides a friendly name for entity instances in downstream experiences.

Add time series data (after binding static data)

Next, bind time series data to entity types in your ontology (preview) item.

1. Follow the steps described earlier for static data to start adding data to the entity type and select your data source. You can select a source from OneLake or Eventhouse.

2. For the Binding type, select Timeseries (time series). Select the Source data timestamp column that contains the timestamp values.

3. Under Bind your properties, you see a Static section and a Timeseries (time series) section.

   In the Static section, bind source columns to the properties that are defined as the entity type key. If you need to update the key, you can add more static data now by selecting + Add static property.

   In the Timeseries (time series) section, continue defining properties by selecting source columns and entering names for each one.

   

4. Select Save to save your time series data binding.

5. Verify the bindings by viewing an updated summary of your data bindings in the Bindings tab, and a summary of properties (including properties added during the recent data binding) in the Properties tab.

Edit or delete data binding

You can edit or delete data bindings in the Entity type configuration pane, in the Bindings tab.

Next to the data binding name, select ... to open its options. From there, you can edit properties or delete the data binding.

Limitations and troubleshooting

Data binding has the following limitations:

• You can't use lakehouses with OneLake security enabled as data sources for bindings. If a lakehouse has OneLake security enabled, you can't use it as a data source in ontology.
• Ontology only supports managed lakehouse tables (located in the same OneLake directory as the lakehouse), not external tables that show in the lakehouse but reside in a different location.
• Changing the lakehouse table name after mappings are created may result in problems accessing data in the preview experience.
• The ontology graph does not support delta tables with column mapping enabled. Column mapping can be enabled manually, or is enabled automatically on lakehouse tables where column names have certain special characters, including ,, ;, {}, (), \n, \t, =, and space. It also happens automatically on the delta tables that store data for import mode semantic model tables.
• Each entity type supports one static data binding. You can't combine static data from multiple sources for a single entity type.
  • You must use OneLake-backed sources for static data.
  • Entity types do support bindings from multiple time series sources. You can bind time series data from both eventhouse and lakehouse sources.

Troubleshooting

For troubleshooting tips related to data binding, see Troubleshoot ontology (preview).