data platforms
Databricks Lakehouse + SQL Ontologies
Timbr brings Databricks together with other data platforms and business systems through a virtual ontology of shared business concepts, relationships, and measures. Analysts, applications, and AI agents query governed concepts instead of raw tables. No data movement. No duplicated logic.
Model Business Concepts in SQL
Transform Databricks tables into reusable business concepts with relationships, hierarchies, and measures defined directly in SQL.
Native Unity Catalog Integration
Expose ontology-based models inside Databricks workflows with no data movement or duplicated logic.
Business Context Across Systems
Connect Databricks with other data platforms and business systems through one virtual ontology of shared business meaning.
Agent-Ready Semantic Access
Give AI agents governed concepts and relationships to query instead of raw schemas and disconnected tables.
How Timbr Connects Databricks to Business Meaning
- Unified Ontology Model: Represent Databricks catalogs, schemas, and datasets as business concepts connected by relationships, hierarchies, and measures.
- Unity Catalog Integration: Expose ontology models inside Databricks workflows while preserving centralized governance and access control.
- Cross-System Context: Connect Databricks with Snowflake, Salesforce, SAP, ServiceNow, Oracle, and operational databases through one virtual ontology.
- Agent-Ready Querying: AI agents query governed concepts and relationships instead of raw lakehouse schemas, reducing prompt complexity and improving consistency.
Features in Action
- Virtual Ontology Across Systems: Model data from Databricks, Snowflake, Salesforce, SAP, ServiceNow, Oracle, and operational databases through shared business concepts.
- Relationship-Based Querying: Query connected entities such as customers, accounts, products, contracts, and transactions without manually writing joins.
- Reusable Measures and Logic: Define measures, rules, and business logic once, then reuse them across SQL, notebooks, BI tools, applications, and AI agents.
- Governed Agent Access: Give AI agents access to approved concepts, relationships, and measures instead of disconnected schemas and scattered definitions.
Impact
- Query Business Concepts, Not Raw Schemas: Analysts, applications, and AI agents work with governed concepts and relationships instead of manually navigating lakehouse tables.
- Connect Meaning Across Systems: Create one virtual ontology across Databricks and other systems, giving users a consistent business view without moving data.
- Govern AI and Analytics Together: Apply access controls, row-level security, measures, and business rules consistently across SQL, BI, applications, and agent queries.
- Reuse Logic Across Every Tool: Define KPIs, relationships, and business rules once, then make them available across notebooks, dashboards, APIs, and AI agents.
