Calculated columns
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allow you to define custom formulas to generate or transform data using logical, mathematical, statistical, or text-based operations. These formulas are applied after other generators like mockers or AI synthesis, ensuring that all base data is available before the calculation is performed.
To clean or reformat data (e.g. trimming, date formatting)
To apply conditional logic (e.g. gender-based name generation)
To compute derived values from other columns (e.g. sales + tax)
To categorize or flag data based on specific criteria
For straightforward mock data generation without dependencies
When no conditional logic is needed
If the column is already correctly populated or handled by simpler generators
Follow the interactive guide below to apply a calculated column formula.
Calculated columns follow a structured expression syntax combining functions, column references, constants, and mockers.
Column reference: [ColumnName] for same-table columns
Functions: IF(), AND(), DATE(), etc.
Mockers: Use MOCK_FIRST_NAME, or MOCK_CONSISTENT_FIRST_NAME for consistent mapping
Operators: +, -, *, /, =, <>, <, >
Constants: Use strings "text", numbers 100, or dates DATE(2020, 12, 31)
Avoid: Column names starting with _, which will cause formula errors
To insert mock data dynamically within formulas:
Type MOCK_ and choose from the autosuggest list
Use names like MOCK_FIRST_NAME, MOCK_COMPANY_EMAIL
For consistent mapping, use MOCK_CONSISTENT_FIRST_NAME, etc.
Calculated columns give you full control over how synthetic values are created or transformed. They’re ideal for applying custom logic while retaining flexibility in the data generation process.
Calculated Columns
Categorical, Discrete, Continuous