Databricks Semantic Layer with Bonnard

Bonnard gives you a Databricks semantic layer defined in YAML, version-controlled, and queryable from AI agents, React components, and REST APIs. If your team runs Unity Catalog, Delta Lake tables, or Databricks SQL warehouses, Bonnard connects directly and exposes governed metrics through MCP, React SDK, REST API, and markdown dashboards.

How does Bonnard connect to Databricks?

Add Databricks as a datasource in your Bonnard project. Define the connection in your datasources.yml:

# datasources.yml
datasources:
  - name: databricks_warehouse
    type: databricks
    workspace_url: https://your-workspace.cloud.databricks.com
    token: ${DATABRICKS_TOKEN}
    catalog: main
    schema: analytics
    http_path: /sql/1.0/warehouses/abc123

Then run:

bon datasource add databricks_warehouse
bon deploy

Bonnard authenticates with your Databricks workspace, introspects your catalog and schema, and deploys your semantic layer. Works with both Databricks SQL warehouses and all-purpose clusters. Your lakehouse data is queryable through every Bonnard surface within minutes.

What do you get?

Once connected, your Databricks data is available through four surfaces:

MCP server. Run bon mcp and your AI agents (Claude, ChatGPT, Cursor) query governed Databricks metrics with row-level security. Generate publishable keys per tenant for customer-facing agentic analytics.

React SDK. Drop BarChart, LineChart, and BigValue components into your product. Every chart queries your Databricks lakehouse through the semantic layer with multi-tenant access controls applied automatically.

REST API. Query metrics programmatically from any language or platform. Type-safe queries with the TypeScript SDK or raw HTTP from anywhere.

Markdown dashboards. Author dashboards in markdown, deploy with bon deploy, and share governed views with your team or customers.

How does Bonnard compare to native Databricks analytics?

Capability	Databricks native	Bonnard + Databricks
Metric definitions	Unity Catalog metrics (preview)	YAML semantic layer (version-controlled)
AI agent access	Genie / AI functions	MCP server with publishable keys
Embedded analytics	Databricks SQL dashboards	React SDK with multi-tenant auth
Dashboards	Databricks SQL dashboards	Markdown dashboards, deployed via CLI
Multi-tenancy	Unity Catalog + workspace isolation	Publishable keys + row-level security
Pre-aggregation	Materialized views (Delta)	Automatic pre-aggregation cache
dbt integration	dbt-databricks adapter	bon datasource add --from-dbt imports models
Access control	Unity Catalog RBAC	YAML-defined RBAC + audit logging
Deployment	Workspace notebooks	bon deploy (no restart, no SSH)

FAQ

Does Bonnard support Databricks?

Yes. Databricks is a first-class Bonnard datasource. Works with Databricks on AWS, Azure, and GCP. Configure your workspace URL, token, and SQL warehouse path, then deploy.

Does Bonnard work with Unity Catalog?

Yes. Specify your catalog and schema in the datasource config. Bonnard introspects Unity Catalog tables and views. You define cubes and measures in YAML on top of your existing catalog structure.

Can I use pre-aggregations with Databricks?

Yes. The pre-aggregation cache handles this automatically. Define rollups in your cube YAML files and Bonnard builds and refreshes them on schedule. This reduces Databricks SQL warehouse compute and speeds up repeated queries.

Can I import dbt models from Databricks?

Yes. Run bon datasource add --from-dbt pointed at your dbt project using the dbt-databricks adapter. Bonnard imports your models as cubes and your metrics as measures. Layer the semantic layer on top without rewriting transformations.