Dimensions

dimensions:
  # Simple string dimension
  - name: customer_name
    type: string
    description: "Customer full name"
    expr: name

  # With samples and synonyms
  - name: order_status
    type: string
    description: "Current order status"
    expr: status
    samples: ["pending", "processing", "shipped", "delivered"]
    synonyms: ["status", "order_state", "fulfillment_status"]

  # Categorical data
  - name: product_category
    type: string
    description: "Product category classification"
    expr: category
    samples: ["Electronics", "Clothing", "Home & Garden", "Sports"]

dimensions:
  # Integer values
  - name: quantity
    type: number
    description: "Quantity of items"
    expr: quantity
    samples: [1, 5, 10]

  # Decimal values
  - name: discount_percent
    type: number
    description: "Discount percentage applied"
    expr: discount_percent
    samples: [0, 10, 25, 50]

  # IDs
  - name: order_id
    type: number
    description: "Unique order identifier"
    expr: id

dimensions:
  # Simple date
  - name: order_date
    type: date
    description: "Date when order was placed"
    expr: order_date
    synonyms: ["purchase_date", "date_ordered"]

  # Birth date
  - name: birth_date
    type: date
    description: "Customer birth date"
    expr: birth_date

dimensions:
  # Timestamp
  - name: created_at
    type: datetime
    description: "Timestamp when record was created"
    expr: created_at

  # Last updated
  - name: updated_at
    type: datetime
    description: "Last update timestamp"
    expr: updated_at

dimensions:
  # Simple boolean
  - name: is_first_order
    type: boolean
    description: "Whether this is customer's first order"
    expr: is_first_order

  # Active status
  - name: is_active
    type: boolean
    description: "Whether the customer is active"
    expr: is_active

dimensions:
  # Extract year from date
  - name: order_year
    type: number
    description: "Year of the order"
    expr: EXTRACT(YEAR FROM order_date)

  # Extract month
  - name: order_month
    type: number
    description: "Month of the order"
    expr: EXTRACT(MONTH FROM order_date)

  # Concatenate fields
  - name: full_address
    type: string
    description: "Complete address"
    expr: CONCAT(street, ', ', city, ', ', state, ' ', zip)

  # Conditional logic
  - name: order_size_category
    type: string
    description: "Order size classification"
    expr: |
      CASE 
        WHEN total_amount >= 1000 THEN 'Large'
        WHEN total_amount >= 500 THEN 'Medium'
        ELSE 'Small'
      END
    samples: ["Large", "Medium", "Small"]

  # Mathematical calculation
  - name: profit_margin_percent
    type: number
    description: "Profit margin as a percentage"
    expr: ((revenue - cost) / NULLIF(revenue, 0)) * 100

dimensions:
  - name: revenue
    type: number
    expr: total_amount
    synonyms: ["sales", "income", "total_sales", "gross_revenue"]

  - name: customer_name
    type: string
    expr: name
    synonyms: ["name", "client_name", "customer"]

  - name: order_date
    type: date
    expr: order_date
    synonyms: ["purchase_date", "date_ordered", "transaction_date", "sale_date"]

dimensions:
  - name: order_status
    type: string
    description: "Current order status"
    expr: status
    samples:
      ["pending", "processing", "shipped", "delivered", "cancelled", "returned"]

  - name: customer_segment
    type: string
    description: "Customer value segmentation"
    expr: segment
    samples: ["High Value", "Medium Value", "Low Value", "At Risk", "New"]

  - name: product_rating
    type: number
    description: "Product rating (1-5 stars)"
    expr: rating
    samples: [1, 2, 3, 4, 5]

dimensions:
  # Date
  - name: order_date
    type: date
    expr: order_date

  # Year
  - name: order_year
    type: number
    expr: EXTRACT(YEAR FROM order_date)

  # Quarter
  - name: order_quarter
    type: number
    expr: EXTRACT(QUARTER FROM order_date)

  # Month
  - name: order_month
    type: number
    expr: EXTRACT(MONTH FROM order_date)

  # Month name
  - name: order_month_name
    type: string
    expr: TO_CHAR(order_date, 'Month')

  # Week
  - name: order_week
    type: number
    expr: EXTRACT(WEEK FROM order_date)

  # Day of week
  - name: order_day_of_week
    type: string
    expr: TO_CHAR(order_date, 'Day')

dimensions:
  - name: country
    type: string
    expr: country
    samples: ["United States", "Canada", "United Kingdom"]

  - name: state
    type: string
    expr: state
    samples: ["CA", "NY", "TX", "FL"]

  - name: city
    type: string
    expr: city

  - name: postal_code
    type: string
    expr: postal_code

  - name: region
    type: string
    description: "Geographic region grouping"
    expr: |
      CASE 
        WHEN state IN ('CA', 'OR', 'WA') THEN 'West'
        WHEN state IN ('NY', 'MA', 'PA') THEN 'Northeast'
        WHEN state IN ('TX', 'FL', 'GA') THEN 'South'
        ELSE 'Other'
      END
    samples: ["West", "Northeast", "South", "Other"]

dimensions:
  - name: age_group
    type: string
    description: "Customer age group"
    expr: |
      CASE 
        WHEN age < 18 THEN 'Under 18'
        WHEN age BETWEEN 18 AND 24 THEN '18-24'
        WHEN age BETWEEN 25 AND 34 THEN '25-34'
        WHEN age BETWEEN 35 AND 44 THEN '35-44'
        WHEN age BETWEEN 45 AND 54 THEN '45-54'
        WHEN age >= 55 THEN '55+'
        ELSE 'Unknown'
      END
    samples: ["18-24", "25-34", "35-44", "45-54", "55+"]

  - name: lifetime_value_tier
    type: string
    description: "Customer lifetime value tier"
    expr: |
      CASE 
        WHEN lifetime_value >= 10000 THEN 'Platinum'
        WHEN lifetime_value >= 5000 THEN 'Gold'
        WHEN lifetime_value >= 1000 THEN 'Silver'
        ELSE 'Bronze'
      END
    samples: ["Platinum", "Gold", "Silver", "Bronze"]