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- Learn the fundamentals of Microsoft Fabric IQ, including how ontologies create a shared business vocabulary, the components that make up the Fabric IQ ecosystem, and how ontology modeling differs from traditional data modeling approaches.
By the end of this module, you'll be able to:
- Explain what Fabric IQ is and how ontologies define business vocabulary
- Describe the role of ontology items in creating entity types, properties, and relationships
- Distinguish between the roles of each Fabric IQ component: ontology items, data agents, Graph in Microsoft Fabric, and Power BI semantic models
- Compare ontology modeling's concept-driven approach with traditional use-case-driven data modeling
- Learn how to create ontologies in Fabric IQ by building manually or generating from Power BI semantic models, then bind ontology definitions to lakehouse tables and eventhouse streams.
By the end of this module, you'll be able to:
- Evaluate two approaches for creating an ontology in Fabric IQ
- Build an ontology manually by working with entity types, properties, keys, and relationships
- Generate an ontology from a Power BI semantic model
- Connect an ontology to data sources and preview the ontology
- Learn how to explore entity instances, visualize entity connections in the relationship graph, and filter ontology data using the Query builder with filters and components in Microsoft Fabric IQ.
By the end of this module, you'll be able to:
- Explore entity instances to see real records from your data sources in the ontology
- Expand the relationship graph to visualize entity connections
- Filter and explore ontology data using the Query builder with filters and components
- Learn how to create a Fabric data agent that uses a Microsoft Fabric IQ ontology as its data source, configure agent instructions to improve accuracy, test natural language queries, and publish the agent for colleagues.
By the end of this module, you'll be able to:
- Explain how an ontology provides business vocabulary that a Fabric data agent uses as a data source
- Create a Fabric data agent and connect it to an ontology as a data source
- Configure agent instructions to improve query accuracy
- Test data agent responses by asking natural language questions and reviewing generated queries
- Publish a Fabric data agent to make it available to colleagues
