OntoBricks 0.8.0

Turn your Databricks tables into a living knowledge graph — in four clicks.

Website · Demos & Screenshots · GitHub · Documentation

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Every enterprise has the data. Almost none have the meaning. Your Lakehouse stores billions of rows, but nothing captures what they represent — that a Customer holds Contracts, which contain Invoices, governed by rules only your senior engineers remember.

OntoBricks closes that gap. It is a Databricks-native app that turns Unity Catalog tables into an explorable, queryable knowledge graph — design the meaning, let AI map it to your data, and navigate the result visually or expose it to your favorite LLM. No separate graph platform, no RDF expertise, no months-long integration project.

Why OntoBricks

• From schema to graph in minutes, not months. LLM-powered automation drives the whole pipeline — import metadata, generate the ontology, map the data, materialize the graph.
• Lives inside Databricks. Deploys as a Databricks App and reuses what you already have: Unity Catalog, SQL Warehouse, Model Serving, Lakebase. Your triples stay in your Lakehouse.
• Standards under the hood, simplicity on top. OWL, R2RML and SPARQL power the engine; everything executes as Spark SQL. Your users never touch RDF.
• Built for the AI era. Publish any domain to LLM agents over the Model Context Protocol (MCP) and let them reason over governed, explainable business semantics.
• Governed by design. Versioned domains, DRAFT → IN-REVIEW → PUBLISHED lifecycle, single-editor locking, and an append-only review audit trail.

From tables to a knowledge graph in four clicks

Step	Action	What happens	Powered by
1	Import Metadata — Domain > Metadata	Fetches table & column metadata from Unity Catalog	Unity Catalog
2	Generate Ontology — Ontology > Wizard	The LLM designs entities, relationships & attributes from your metadata	Model Serving
3	Auto-Map — Mapping > Auto-Map	The LLM generates SQL mappings for every entity and relationship	Model Serving
4	Synchronize — Knowledge Graph > Status	Executes the mappings and populates the triple store	SQL Warehouse

Prefer full control? Every step is also available as a guided manual workflow — design, map, build, query, and reason at column-level precision. See the User Guide.

What's inside

Design ontologies visually or with AI

Drag-and-drop entities, relationships and inheritance on the OntoViz canvas, or import industry standards in one click (FIBO, CDISC, IOF, HL7 FHIR R4/R4B/R5) and your own OWL/RDFS. A floating AI Assistant edits your ontology through natural language. Catch design issues early with the built-in Ontology Pitfalls Detector (19 structural, logical and semantic checks).

Map to your data — automatically

Let the LLM generate SQL and column mappings for every entity and relationship, then refine with live data preview. Mappings compile to W3C-compliant R2RML you never have to write. Schema-drift warnings flag upstream renames and drops before they break your graph.

Build, reason and explore

Materialize triples incrementally, then reason over the graph with OWL 2 RL inference, SWRL rules and SHACL validation. Explore interactively: two-phase search, N-hop neighbour expansion, cross-domain bridge navigation, community detection (Louvain, Label Propagation, Greedy Modularity), and explainable cohort discovery. Query everything through an auto-generated GraphQL API. Details in the User Guide and Cohort Discovery.

Publish to AI agents (MCP)

Expose your knowledge graph to Cursor, Claude Desktop or the Databricks Playground over the Model Context Protocol. Each domain decides exactly what it publishes — which tools, which datasets, which class actions and virtual attributes — right down to ontology-only domains that expose a single describe_ontology tool. See MCP Integration.

Govern the whole lifecycle

Every domain version carries a DRAFT / IN-REVIEW / PUBLISHED status enforced server-side. A single editor holds a DRAFT at a time (auto-releasing leases, admin take-over), a business-friendly review workflow collects sign-offs with a configurable quorum, and every decision is persisted append-only. Move domains between environments with the OBX export/import (UI or CLI).

Pluggable graph engine

Pick a backend per domain: Lakebase (Postgres) by default, Lakehouse (governed Delta triple tables, zero extra infra), Neo4j, or No Backend for ontology-only domains that publish their ontology with no graph built (Mapping and Knowledge Graph are disabled and the MCP surface serves only ontology information). Connection config stays workspace-global. Full reference: Lakebase Graph DB · Graph DB Integration.

Runs entirely on your Databricks

OntoBricks is not a separate platform. It deploys as a Databricks App and uses the services you already run — Unity Catalog for storage and metadata, a SQL Warehouse for execution, Model Serving for LLM automation, and Lakebase for the registry and graph store. Your data and your triples never leave your workspace. Architecture deep-dive: architecture.md.

Get started

Try it locally

git clone <repository-url> && cd OntoBricks
uv sync --frozen --extra lakebase  # install the committed public-PyPI lock
cp .env.example .env        # set your Databricks host, token, warehouse ID
scripts/start.sh            # open http://localhost:8000

Always use --frozen for routine uv sync / uv run commands. The lock records package indexes as https://pypi.org/simple and immutable artifact downloads on https://files.pythonhosted.org; do not commit internal pypi-proxy.dev.databricks.com URLs.

Deploy to Databricks Apps

databricks auth login --host https://<workspace>
# edit scripts/deploy.config.sh (profile, warehouse, registry, Lakebase), then:
make deploy

Prerequisites (Databricks workspace with Apps enabled, a SQL Warehouse, a Lakebase Autoscaling database, and a Unity Catalog Volume) and the full, idempotent deploy checklist — including one-click graph-DB provisioning and permission bootstrap — are in the Get Started and Deployment Guide.

Existing 0.7.x operators should follow Updating to 0.8.0 for backup, migration, deployment, verification, and rollback steps.

The bundle syncs only runtime source, the MCP and graph-job files, dependency locks, and the in-app Help Center set. databricks.yml sync.exclude is authoritative; .databricksignore is a reviewer/test mirror and is not read by the Databricks CLI.

Documentation

• Documentation hub — start here
• Value proposition — the business case & go-to-market
• User Guide · Features · Architecture · API
• MCP · Graph DB · Import / Export
• Developing OntoBricks — build, test, contribute

Product overview, screenshots and videos: ontobricks.org.

Project Support

Please note that all projects in the /databrickslabs github account are provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of these projects.

Any issues discovered through the use of this project should be filed as GitHub Issues on the Repo. They will be reviewed as time permits, but there are no formal SLAs for support.