Data Generator
Generate realistic mock data for testing and development. Names, emails, phone numbers, addresses, and more.
Data Types
Generate names, emails, phone numbers, addresses, passwords, UUIDs, and more.
Customization
Configure options for each data type like password complexity or number ranges.
Export Options
Copy individual items or download all data as a CSV file.
About the Mock Data Generator
This mock data generator lets you define a record schema field by field — names, emails, dates, numbers, booleans — and generates records matching it, rather than producing a fixed shape you then have to reshape.
A mock data generator is most useful when the output matches your actual data model. A fixed set of columns always needs reshaping before it fits your table, and that reshaping is often more work than the data was worth.
Defining the schema first avoids that. Choose exactly the fields your entity has and the type each should hold, and the generated records drop straight into your table, fixture or API stub without transformation.
Type variety is what makes generated data useful for testing. Dates exercise date parsing, booleans exercise conditional logic, numbers exercise validation ranges, and text fields with awkward characters exercise escaping. All records are fictional and generated in your browser.
How to use the Mock Data Generator
- Define your fields. Add each field your entity needs.
- Set the type per field. Choose name, email, date, number, boolean or text.
- Set the record count. Choose how many records to generate.
- Export. Take the records for your fixture, stub or table.
Mock Data Generator features
- Custom field-by-field schema definition
- Name, email, date, number, boolean and text types
- Output shaped to your own data model
- Bulk generation
- Entirely fictional records
- Runs entirely in your browser
Frequently asked questions
How is this different from the other data generators?
Here you define the schema field by field, so the output matches your data model without reshaping.
Which field types are available?
Names, emails, dates, numbers, booleans and free text, which between them exercise most validation paths.
Why does type variety matter in test data?
Dates test parsing, booleans test conditionals, numbers test ranges and awkward text tests escaping.
Is the data fictional?
Yes. Nothing corresponds to a real person or organisation.
Is it generated locally?
Yes, entirely in your browser.