Introduction
Timbr and Databricks give enterprise teams a governed way to bring business meaning into lakehouse workflows. Databricks provides the scalable foundation for data processing, analytics, AI, and governance. Timbr adds an ontology-based semantic layer that models business concepts, relationships, measures, and rules, making them accessible directly through SQL, notebooks, BI tools, applications, and AI agents.
This article explores how Timbr integrates with Databricks and how Timbr’s ontology-based models are exposed through Databricks Unity Catalog, allowing users to work with governed business concepts instead of raw schemas.
Timbr – The Intelligent Semantic Layer
Timbr enables users to create virtual data models that present Databricks datasets as business concepts enriched with semantic relationships, governed measures, and explicit business context. These models can be consumed by the tools and applications already used for analytics, data science, BI, and AI workflows.
A virtual data model in Timbr is organized as a hierarchy of business concepts – an ontology – with logical business definitions, measures, relationships that replace manual JOIN statements, and the ability to integrate data from multiple sources into one contextual business model.
The models are versioned, governed, and controlled, supporting metadata management, data discovery, and different levels of access. Data models in Timbr can be imported automatically from an existing data model or created using standard SQL statements. When mapping data to the model, users can apply transformations and functions supported by the connected database, while keeping business logic managed in the semantic layer.
Semantic Data Virtualization with Timbr and Databricks
Timbr and Databricks together provide a semantic virtualization layer for querying governed business concepts across large-scale data. Instead of forcing users to work directly with physical tables, schemas, and joins, Timbr maps underlying Databricks data into concepts, relationships, measures, and rules.
This allows analysts, engineers, BI users, applications, and AI agents to query the business model while Databricks continues to provide the scalable execution environment. Data does not need to be moved or duplicated, and semantic logic does not need to be rebuilt separately for every notebook, dashboard, application, or AI workflow.
For larger enterprise environments, Timbr’s ontology can also span Databricks and the broader business ecosystem, helping teams maintain consistent meaning across multiple systems while still consuming governed concepts inside Databricks.
Timbr & Databricks' Unity Catalog native integration
Timbr exposes ontology-based model metadata in Databricks Unity Catalog, giving users a clearer way to discover, understand, and consume governed business concepts inside Databricks workflows. Users can also enable SSO from Databricks to Timbr to align security and access controls.
Through this native integration, users can query Timbr business concepts directly from Databricks notebooks using SQL, Python, R, or Scala. Databricks Catalog Explorer can surface Timbr semantic schemas, giving teams a clearer understanding of concepts, relationships, and governed measures available for analytics and AI workflows.
Through Timbr’s native Databricks integration, organizations can create scalable ontology-based semantic models, query governed business concepts, reuse relationships and measures, and make Databricks workflows more accessible to analysts, BI users, applications, and AI agents.
Timbr helps teams move from raw schemas to governed business meaning, while preserving Databricks as the lakehouse foundation for processing, analytics, and AI.
Contact us today to create your Semantic Layer with Databricks.
