ontology consumption
Databricks Unity Catalog
Timbr integrates with Unity Catalog to expose ontology-based models inside Databricks workflows. Timbr’s ontology can span Databricks and other enterprise systems, giving analysts, applications, and AI agents governed access to connected business concepts, relationships, and measures rather than raw schemas.
Key Capabilities
Ontology Models in Databricks
Expose Timbr concepts, relationships, and measures inside Databricks workflows through Unity Catalog-connected access.
Governed data access
Apply permissions, row-level security, and model-level policies across SQL, notebooks, BI tools, and agent queries.
Business Context Discovery
Surface business definitions, relationships, and governed measures in Databricks so users can understand what the data means.
Agent-Ready Consumption
Give AI agents governed ontology concepts to query instead of relying only on raw catalogs, tables, and schema metadata.
How it Works
- Expose Business Concepts: Make ontology concepts, relationships, and measures visible through Unity Catalog without moving or duplicating data.
- Enable Single Sign-On: Let users access Timbr from Databricks through SSO while aligning governance and permissions.
- Support SQL, Python, R, and Scala: Consume Timbr concepts in notebooks and SQL endpoints using standard Databricks tools.
- Surface Context in Catalog Explorer: Show business definitions, relationships, and semantic context directly inside Databricks discovery workflows.
- Enable MCP Agent Access: Let AI agents connect through Timbr’s MCP server to access governed ontology concepts and relationships.
Key Benefits for Data Consumers
- Reusable Semantic Models: Expose governed business concepts, relationships, and measures directly inside Databricks workflows.
- Governance Alignment: Apply model-level policies, row-level security, and permissions consistently across users, tools, and agents.
- Semantic Discovery: Make business definitions, labels, and relationships available in Databricks so users can understand the context behind the data.
- Cross-Language Consumption: Query the same semantic model through SQL, Python, R, Scala, notebooks, SQL endpoints, BI tools, and APIs.
- Governed Agent Access: Let AI agents query approved business concepts and relationships instead of raw schemas and disconnected metadata.
