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Fields and Types

Understand dimensions, measures, and data types in Strata.

Learning Objectives​

After completing this guide, you will be able to:

  • Distinguish between dimensions and measures
  • Choose appropriate data types
  • Configure field properties (format, display types)
  • Understand when to use different field options

Dimensions vs Measures​

Dimensions​

Dimensions are categorical fields used for grouping and filtering. They represent attributes of your data.

Characteristics:

  • Used in GROUP BY clauses
  • Can be filtered
  • Typically lower cardinality
  • Examples: Customer Name, Product Category, Order Date

Example:

- type: dimension
  name: Customer Name
  data_type: string
  expression:
    sql: customer_name

Measures​

Measures are aggregatable metrics. They represent quantitative values that can be summed, averaged, or counted.

Characteristics:

  • Used with aggregation functions (SUM, AVG, COUNT)
  • Cannot be used in GROUP BY
  • Examples: Total Revenue, Order Count, Average Order Value

Example:

- type: measure
  name: Total Revenue
  data_type: decimal
  expression:
    sql: sum(amount)

Data Types​

Strata supports the following data types:

string​

Text values. Use for names, descriptions, codes, etc.

- type: dimension
  name: Product Name
  data_type: string
  expression:
    sql: product_name

integer​

Whole numbers. Use for counts, IDs, quantities.

- type: dimension
  name: Order ID
  data_type: integer
  expression:
    primary_key: true
    sql: order_id

bigint​

Large whole numbers. Use for very large integers.

- type: dimension
  name: Customer ID
  data_type: bigint
  expression:
    sql: customer_id

decimal​

Floating-point numbers. Use for monetary values, percentages, measurements.

- type: measure
  name: Total Revenue
  data_type: decimal
  expression:
    sql: sum(amount)

date​

Date values (without time). Use for date dimensions.

- type: dimension
  name: Order Date
  data_type: date
  expression:
    sql: order_date
  grains:
    - day
    - week
    - month
    - quarter
    - year

date_time​

Timestamp values (with time). Use for precise timestamps.

- type: dimension
  name: Created At
  data_type: date_time
  expression:
    sql: created_at
  grains:
    - raw
    - hour
    - day
    - week
    - month

boolean​

True/false values.

- type: dimension
  name: Is Active
  data_type: boolean
  expression:
    sql: is_active

binary​

Binary data. Rarely used in analytics.

Field Properties​

Display Types​

Control how fields are rendered in the UI:

  • default: Standard text display
  • html: Render as HTML
  • url: Display as clickable link
  • email: Display as email link
  • phone_number: Format as phone number
  • image: Display as image
- type: dimension
  name: Product Image
  data_type: string
  display_type: image
  expression:
    sql: image_url

Format​

Control how values are displayed using the format property (shortcut string or hash). See Field format for types, shortcuts, and defaults.

- type: measure
  name: Total Revenue
  data_type: decimal
  format: currency:2
  expression:
    sql: sum(amount)

Hidden Fields​

Hide fields from the UI while keeping them available for calculations:

- type: dimension
  name: Internal ID
  data_type: integer
  hidden: true
  expression:
    sql: internal_id

Value Listing (Dimensions Only)​

Control whether dimension values are listed in filters:

# Disable listing for high-cardinality dimensions
- type: dimension
  name: Customer ID
  data_type: integer
  disable_value_listing: true
  expression:
    sql: customer_id

# Limit list size
- type: dimension
  name: Product Category
  data_type: string
  value_list_size: 500
  expression:
    sql: category

Synonyms​

Provide alternative names for a field. Synonyms help AI-powered search and natural language queries find the right field when users refer to it by different names.

- type: dimension
  name: Customer Name
  data_type: string
  synonyms:
    - client name
    - buyer name
    - account name
  expression:
    sql: customer_name

Grains (Date/DateTime Only)​

Specify supported granularities for date fields:

- type: dimension
  name: Order Date
  data_type: date
  grains:
    - day
    - week
    - month
    - quarter
    - year
  expression:
    sql: order_date

Available grains: raw, second, minute, hour, day, week, month, quarter, year

Complete Example​

fields:
  # Primary key dimension
  - type: dimension
    name: Order ID
    description: Unique order identifier
    data_type: integer
    expression:
      primary_key: true
      sql: order_id

  # Date dimension with multiple grains
  - type: dimension
    name: Order Date
    description: Date when order was placed
    data_type: date
    grains:
      - day
      - week
      - month
      - quarter
      - year
    expression:
      sql: order_date

  # High-cardinality dimension (no value listing)
  - type: dimension
    name: Customer ID
    description: Customer identifier
    data_type: integer
    disable_value_listing: true
    expression:
      sql: customer_id

  # Formatted measure with synonyms
  - type: measure
    name: Total Revenue
    description: Sum of all order amounts
    data_type: decimal
    format: currency:2
    synonyms:
      - total sales
      - gross revenue
      - sales amount
    expression:
      sql: sum(amount)

  # Count measure
  - type: measure
    name: Order Count
    description: Number of orders
    data_type: integer
    expression:
      sql: count(*)

Best Practices​

  1. Use appropriate data types - Match your database column types
  2. Add descriptions - Help users understand what each field represents
  3. Set grains for dates - Enable flexible date grouping
  4. Disable value listing for high-cardinality dimensions (IDs, timestamps)
  5. Use format for better UX (currency, percentages)
  6. Mark primary keys - Helps with query optimization

Next Steps​