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Define a schema and generate realistic test data in seconds.
The Mock Data Generator lets you define a JSON schema and instantly generate realistic test data for development, testing, and demonstration purposes. Creating realistic sample data by hand is tedious and often produces data that is too uniform to catch edge cases. This tool generates diverse, realistic data based on your schema definition, saving hours of manual test data creation and ensuring your tests cover a wider range of scenarios.
To use the generator, either paste an existing JSON structure as a template or define your schema using the visual editor. For each field, you can specify the data type (string, number, boolean, array, object) and optionally choose a semantic type like 'name', 'email', 'phone', 'address', 'credit card', 'UUID', 'date', 'lorem ipsum', or 'enum'. The generator then produces data that satisfies both the structural constraints and semantic expectations of each field.
The generator includes dozens of built-in generators for common data types. Name generators produce culturally diverse first and last names. Address generators create realistic street addresses, cities, states, and postal codes with country-specific formats. Financial generators produce valid credit card numbers (that pass Luhn check but are not real), IBANs, and currency amounts. For each generator type, you can configure parameters like locale, length, and formatting options.
Advanced features include conditional generation (generate X fields if a condition is met), custom enum sets, cross-field references (ensure consistency between related fields like city and zip code), and support for nested and recursive schemas. You can generate any number of records, from a single object to thousands of rows for load testing. The output can be viewed inline or exported as JSON, CSV, or SQL INSERT statements.
The tool is particularly valuable for frontend developers who need realistic API responses to build UIs before the backend is ready. By defining the expected API response schema, you can generate diverse mock data that exercises your UI components across different states. Backend developers use the generated SQL INSERT statements to populate test databases with realistic volumes of data for performance testing.
For data quality and realism, the generator applies constraints like string length limits, numeric ranges, regex patterns, uniqueness requirements, and null probability. You can also define custom generators using JavaScript expressions for fields that need special logic. The tool ensures generated data conforms to your schema validation rules, so your tests don't fail due to unexpected data shapes but do catch real application bugs.
Yes. The generator supports locale-specific data for names, addresses, phone numbers, and date formats. Currently available locales include en-US, en-GB, zh-CN, ja-JP, de-DE, fr-FR, and more.
Yes, but these are test numbers that pass Luhn validation with correct issuer prefixes. They are randomly generated and not associated with any real accounts. Never use generated numbers in production payment systems.
Yes. For numeric fields you can specify min/max ranges and distribution patterns (uniform, normal, or sequential). For string fields you can provide a regex pattern or an enum of possible values with weights.
There is no hard limit, but generating more than 100,000 records may cause browser performance issues. For very large datasets, you can export in batches or use the streaming output option.