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The semantic layer lets you define metrics, dimensions, and joins once so dazense answers consistently and transparently.
The semantic layer governs metric meaning. It does not replace policy enforcement. For hard controls (PII, scope, contracts), use Trusted Analytics V1.

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Create a semantic model

Create semantics/semantic_model.yml: 
models:
  customers:
    table: customers
    schema: main
    description: Customer master data
    primary_key: customer_id
    dimensions:
      customer_id:
        column: customer_id
      first_name:
        column: first_name
      last_name:
        column: last_name
    measures:
      customer_count:
        type: count

  orders:
    table: orders
    schema: main
    description: All orders placed by customers
    primary_key: order_id
    time_dimension: order_date
    dimensions:
      status:
        column: status
    measures:
      order_count:
        type: count
      total_revenue:
        column: amount
        type: sum
      avg_order_value:
        column: amount
        type: avg
    joins:
      customer:
        to_model: customers
        foreign_key: customer_id
        related_key: customer_id
        type: many_to_one

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Measure types

  • count, sum, avg, min, max, count_distinct

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Add deterministic filters to critical measures

For business-critical metrics (for example revenue), encode exclusions directly in the measure definition: 
total_revenue:
  column: amount
  type: sum
  filters:
    - column: status
      operator: not_in
      value: [returned, return_pending]
This prevents LLM-dependent interpretation drift.

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Join types

  • many_to_one, one_to_one, one_to_many
When a semantic model is present, the agent uses query_metrics for covered questions to avoid ad‑hoc SQL and keep logic governed.

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Mode behavior

  • Direct Analytics: semantic model optional
  • Trusted Analytics V1: semantic model strongly recommended (often required for critical metrics)
  • Trusted Analytics V2: same as V1, with metadata enrichment from OpenMetadata