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Literal Data Generation

Literal data is the simplest data generation option.

A literal value is static text. The exact text is used for every generated row.

Use literal data when you want stable, predictable values rather than random values.

When to use Literal values​

Literal values are useful for:

  • fixed environment labels, e.g. UAT, PROD, local
  • default flags, e.g. enabled, active, pending
  • seeded values for specific test scenarios
  • columns that should remain constant while other columns vary

Basic Example​

Schema text format uses:

  • column name
  • generation rule

For a literal column:

Environment
UAT

If you generate 5 rows, every row in Environment will be UAT.

Multiple Literal Columns​

You can define several static columns at once:

Country
UK
Currency
GBP
Status
ACTIVE

This is useful for creating baseline datasets quickly.

Mixed Example: Literal + Faker + Regex​

Literal values are often combined with Faker and Regex columns.

Environment
UAT
Customer Name
person.fullName
Order Ref
[A-Z]{3}-[0-9]{6}
Is Premium
true

In this example:

  • Environment is always UAT
  • Customer Name varies per row using Faker
  • Order Ref varies per row using Regex
  • Is Premium is always true

Tips​

  • Use Literal for columns that should not vary.
  • Use Faker/Regex for variability and realism.
  • Keep literal values simple and explicit so generated data is easy to reason about.

You can read more about the other generation modes here: