Bonnard Get Started

Get Started with Bonnard

This skill guides you through building and deploying a semantic layer. The user has already run bon init.

IMPORTANT — pacing: Complete ONE phase at a time. Before starting a phase, briefly explain what it does and why. After completing it, show the user what happened and ask if they're ready to move on. Do NOT run through all phases in one go.

Phase 1: Connect a Data Source

Ask the user if they have a warehouse to connect, or want to try a demo dataset first:

# Option A: Use demo data (no warehouse needed)
bon datasource add --demo

# Option B: Import from dbt (if they use it)
bon datasource add --from-dbt

# Option C: Add manually, non-interactive (preferred for agents)
bon datasource add --name my_warehouse --type postgres \
  --host db.example.com --port 5432 --database mydb --schema public \
  --user myuser --password mypassword

# Option D: Add manually, interactive (in user's terminal)
bon datasource add

Supported types: postgres, redshift, snowflake, bigquery, databricks, duckdb.

The demo option adds a read-only Contoso retail dataset with tables like fact_sales, dim_product, dim_store, and dim_customer.

The connection will be tested automatically during bon deploy.

Phase 2: Explore the Data

Before creating cubes, understand what tables and columns are available in your warehouse.

Important: bon query is for querying the deployed semantic layer — it does NOT access your database directly. Use your warehouse's native tools to explore tables.

Options for exploring your data:

• Use your database CLI (e.g., psql for Postgres/Redshift, snowsql for Snowflake, bq for BigQuery) to list tables and columns
• Check your dbt docs or existing documentation for table schemas
• Ask the user for table names and column details if you don't have direct database access
• For the demo dataset, the tables are: contoso.fact_sales, contoso.dim_product, contoso.dim_store, contoso.dim_customer

Note the table names, column names, and data types — you'll use these in Phase 3.

CHECKPOINT: Show the user the tables and columns you found. Ask which table they'd like to model first before proceeding.

Phase 3: Create Your First Cube

Based on what you found in Phase 2, create a file in bonnard/cubes/ for the most important table. A cube maps directly to a database table — define measures for the metrics you want to track and dimensions for the attributes you want to filter and group by. Use the actual table and column names from Phase 2, not placeholder names.

Example using demo data — bonnard/cubes/sales.yaml:

cubes:
  - name: sales
    sql_table: contoso.fact_sales
    data_source: contoso_demo

    measures:
      - name: count
        type: count
        description: Total number of sales transactions

      - name: total_revenue
        type: sum
        sql: sales_amount
        description: Sum of sales revenue

      - name: total_cost
        type: sum
        sql: total_cost
        description: Sum of product costs

      # Filtered measures — match what users actually see on dashboards
      - name: return_count
        type: count
        description: >-
          Sales transactions with returns only (return_quantity > 0).
          For all transactions, use count.
        filters:
          - sql: "{CUBE}.return_quantity > 0"

    dimensions:
      - name: sales_key
        type: number
        sql: sales_key
        primary_key: true

      - name: date
        type: time
        sql: date_key
        description: Sale date

      - name: sales_quantity
        type: number
        sql: sales_quantity
        description: Number of units sold

Key rules:

• Every cube needs a primary_key dimension — it must be unique. If no column is naturally unique, use sql with ROW_NUMBER() instead of sql_table and add a synthetic key. Non-unique PKs cause dimension queries to silently return empty results.
• Every measure and dimension should have a description — descriptions are how AI agents discover and choose metrics (see /bonnard-design-guide)
• Add filtered measures for any metric users track with filters (e.g., "online revenue" vs "total revenue"). If a dashboard card has a WHERE clause beyond a date range, that filter should be a filtered measure.
• Measure descriptions should say what's included and excluded — e.g., "Online channel only. For all channels, use total_revenue."
• Dimension descriptions should include example values for categorical fields — e.g., "Sales channel (values: Online, Store, Reseller)"
• Set data_source to match the datasource name from Phase 1
• Use sql_table with the full schema.table path
• Use sql_table for simple table references, sql for complex queries

Use bon docs cubes for the full reference, bon docs cubes.measures.types for all 12 measure types, bon docs cubes.dimensions.types for dimension types.

Phase 4: Design Views for Your Audience

Before creating view files, ask the user:

• Who will query this data? (e.g., sales managers, product analysts, executives, AI agents)
• What questions do they typically ask? (e.g., "what's revenue by store this month?", "which products are trending?")

Use the answers to decide view names, which measures/dimensions to include, and how to write descriptions. Each persona or use case may warrant its own view.

Views expose a curated subset of measures and dimensions for specific audiences. Name views by what they answer (e.g., sales_performance), not by what table they wrap (e.g., sales_view). A view can combine measures and dimensions from multiple cubes via join_path.

The view description is critical — it's how AI agents decide which view to query. It should answer: what's in here, when to use it, and when to use something else instead. Include key dimension values where helpful.

Example using demo data — bonnard/views/sales_performance.yaml:

views:
  - name: sales_performance
    description: >-
      Retail sales metrics — revenue, cost, and transaction counts by product,
      store, and date. Default view for sales questions. Includes return_count
      for return analysis. For customer demographics, use customer_insights
      instead.
    cubes:
      - join_path: sales
        includes:
          - count
          - total_revenue
          - return_count
          - total_cost
          - date
          - sales_quantity

Use bon docs views for the full reference. See /bonnard-design-guide for principles on naming, descriptions, and view structure.

Phase 5: Validate

Check for errors:

bon validate

Fix any errors before proceeding. Common issues:

• Missing required fields (name, type, sql)
• Unknown measure/dimension types (e.g., text should be string)
• Bad YAML indentation

Validate also warns about:

• Missing descriptions — descriptions help AI agents and analysts discover metrics
• Missing data_source — cubes without an explicit data_source use the default warehouse, which can cause issues when multiple warehouses are configured

Phase 6: Deploy

CHECKPOINT: Before deploying, summarize what will be deployed — list the cubes, views, datasources, and any joins. Ask the user to confirm before running bon deploy.

Log in (if not already) and deploy:

bon login
bon deploy -m "Initial semantic layer with sales cube and overview view"

Deploy requires a -m message describing the changes. It validates, tests connections, uploads cubes/views, and syncs datasource credentials (encrypted) to Bonnard.

After deploying, the output shows what changed (added/modified/removed) and flags any breaking changes. Use bon deployments to see history and bon diff to review changes from any deployment.

Phase 7: Test with Queries

Verify the deployment works using the view name from Phase 4:

# Simple count
bon query '{"measures": ["sales_performance.count"]}'

# With a dimension
bon query '{"measures": ["sales_performance.total_revenue"], "dimensions": ["sales_performance.date"]}'

# SQL format
bon query --sql "SELECT MEASURE(total_revenue) FROM sales_performance"

Test with natural language too. If you've set up MCP (Phase 8), ask an AI agent questions like "what's our total revenue?" and check whether it picks the right view and measure. If it picks the wrong view, the issue is usually the view description — see /bonnard-design-guide Principle 4.

Phase 8: Connect AI Agents (Optional)

Set up MCP so AI agents can query the semantic layer:

bon mcp

This shows setup instructions for Claude Desktop, ChatGPT, Cursor, VS Code, and other MCP clients. The MCP URL is https://mcp.bonnard.dev/mcp.

Next Steps

After the first cube is working:

• Iterate on descriptions — test with real questions via MCP, fix agent mistakes by improving view descriptions and adding filtered measures (/bonnard-design-guide)
• Add more cubes for other tables
• Add joins between cubes (bon docs cubes.joins)
• Build audience-centric views that combine multiple cubes — e.g., a management view that pulls from sales, users, and products cubes
• Add calculated measures (bon docs cubes.measures.calculated)
• Add segments for common filters (bon docs cubes.segments)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: bonnard-data
• Source: bonnard-data/bonnard-cli
• License: MIT
• Homepage: https://www.bonnard.dev

Install and usage instructions live in the source repository linked above.