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Create dimension tables that let you segment your entities for analysis.

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Before You Begin

Before creating a dimension table, ensure you have:
  • An API access token with appropriate permissions
  • Created the entities you want to segment
  • Prepared a SQL query that selects dimension data from your data warehouse
  • Identified entity and dimension columns

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Create a Dimension Table

Create a dimension table with a country dimension: 
curl -X POST "https://api.confidence.dev/v1/dimensionTables" \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "displayName": "User Location",
    "sql": "SELECT * FROM confidence.user_data.location",
    "timestampColumn": {
      "name": "date",
      "type": "COLUMN_TYPE_STRING",
      "repeated": false
    },
    "entityColumnMapping": [
      {
        "column": {
          "name": "user_id",
          "type": "COLUMN_TYPE_STRING",
          "repeated": false
        },
        "entity": "entities/user"
      }
    ],
    "dimensions": [
      {
        "name": "country",
        "type": "COLUMN_TYPE_STRING",
        "repeated": false
      }
    ],
    "dataDeliveredUntilUpdateStrategyConfig": {
      "dailyUpdateConfig": {}
    }
  }'

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Create a Dimension Table with Multiple Dimensions

Track multiple attributes for segmentation: 
curl -X POST "https://api.confidence.dev/v1/dimensionTables" \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "displayName": "User Demographics",
    "sql": "SELECT * FROM user_demographics",
    "timestampColumn": {
      "name": "updated_at",
      "type": "COLUMN_TYPE_STRING",
      "repeated": false
    },
    "entityColumnMapping": [
      {
        "column": {
          "name": "user_id",
          "type": "COLUMN_TYPE_STRING",
          "repeated": false
        },
        "entity": "entities/user"
      }
    ],
    "dimensions": [
      {
        "name": "country",
        "type": "COLUMN_TYPE_STRING",
        "repeated": false
      },
      {
        "name": "age_group",
        "type": "COLUMN_TYPE_STRING",
        "repeated": false
      },
      {
        "name": "is_premium",
        "type": "COLUMN_TYPE_BOOLEAN",
        "repeated": false
      }
    ],
    "dataDeliveredUntilUpdateStrategyConfig": {
      "dailyUpdateConfig": {}
    }
  }'

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Dimension Types

Supported dimension types:
  • COLUMN_TYPE_STRING: Categorical values (country, platform, etc.)
  • COLUMN_TYPE_BOOLEAN: Binary attributes (is_premium, is_active, etc.)
  • COLUMN_TYPE_INTEGER: Numeric categories (age group codes, tier levels, etc.)

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Data Delivery

After creation, the dimension table enters the CREATING state. Confidence runs a sample query to verify the SQL produces the expected columns, then transitions to either ACTIVE or FAILED.

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Next Steps

After creating dimension tables:
  • Use dimensions to segment metrics in experiment analysis
  • Create metrics that you can break down by these dimensions
  • Configure experiments to analyze results by dimension