Snowflake Semantic Layer with Bonnard

Bonnard gives you a Snowflake semantic layer defined in YAML, version-controlled, and queryable from AI agents, React components, and REST APIs. If your team runs dbt models, stores event data, or centralizes reporting in Snowflake, Bonnard connects directly and exposes governed metrics through MCP, React SDK, REST API, and markdown dashboards.

How does Bonnard connect to Snowflake?

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

# datasources.yml
datasources:
  - name: snowflake_warehouse
    type: snowflake
    account: xy12345.us-east-1
    username: BONNARD_USER
    password: ${SNOWFLAKE_PASSWORD}
    database: ANALYTICS
    warehouse: COMPUTE_WH
    schema: PUBLIC
    role: BONNARD_ROLE

Then run:

bon datasource add snowflake_warehouse
bon deploy

Bonnard validates the connection, introspects your schema, and deploys your semantic layer. No drivers to install, no middleware. Your Snowflake data is queryable through every surface Bonnard supports within minutes.

What do you get?

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

MCP server. Run bon mcp and your AI agents (Claude, ChatGPT, Cursor) query governed Snowflake 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 Snowflake warehouse 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 Snowflake analytics?

Capability	Snowflake native	Bonnard + Snowflake
Metric definitions	SQL views, Snowsight worksheets	YAML semantic layer (version-controlled)
AI agent access	None	MCP server with publishable keys
Embedded analytics	Snowsight (internal only)	React SDK with multi-tenant auth
Dashboards	Snowsight	Markdown dashboards, deployed via CLI
Multi-tenancy	Manual role management	Publishable keys + row-level security
Pre-aggregation	Materialized views (manual)	Automatic pre-aggregation cache
dbt integration	Native dbt support	bon datasource add --from-dbt imports models
Access control	Snowflake RBAC	YAML-defined RBAC + audit logging
Deployment	SQL scripts	bon deploy (no restart, no SSH)

Snowflake-specific optimization

Virtual warehouse targeting

Bonnard's pre-aggregation rebuilds and live queries can target different Snowflake virtual warehouses. Run cache rebuilds on a small, always-on XS warehouse. Route ad-hoc queries to a larger warehouse that auto-suspends. This keeps costs low for background work and performance high for interactive queries.

Pre-aggregation and Snowflake costs

Snowflake charges by compute time. Every query spins up a warehouse (or keeps one running). Pre-aggregation reduces the number of warehouse queries by serving repeated requests from cache. For dashboards and AI agents that query the same metrics frequently, pre-aggregation can cut Snowflake compute costs significantly. Define rollups for your most-queried metrics and let cold queries fall through to the warehouse.

Snowpark and Iceberg tables

If your team uses Snowpark for Python/Java UDFs or Iceberg tables for open table formats, Bonnard queries the resulting tables like any other Snowflake table. Define cubes on top of your Snowpark output tables or Iceberg-backed views. The semantic layer doesn't need to know how the data got there.

dbt + Snowflake workflow

A common pattern: dbt transforms raw data into clean tables in Snowflake on a schedule. Bonnard defines business metrics on those tables and serves them to dashboards, AI agents, and applications in real time. Import your dbt models with bon datasource add --from-dbt. The two tools complement each other without overlap.

FAQ

Does Bonnard support Snowflake?

Yes. Snowflake is a first-class Bonnard datasource. All Snowflake editions (Standard, Enterprise, Business Critical) are supported. Configure your connection in YAML, run bon datasource add, and deploy.

Can I use pre-aggregations with Snowflake?

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 Snowflake compute costs and speeds up repeated queries without manual materialized view management.

How does multi-tenancy work with Snowflake?

Generate publishable keys per tenant. Bonnard maps each key to a security context with row-level filters. Every query against your Snowflake warehouse is filtered automatically. No per-tenant Snowflake roles or databases required.

Can I import dbt models on Snowflake?

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

Related

• BigQuery integration -- if your team also uses GCP
• What is a Semantic Layer? -- how metric definitions work
• Bonnard vs Looker -- comparing Looker and Bonnard on Snowflake