Fake Data Generator for JSON mock data, schemas, fixtures, and demos

• Visual JSON schema builder

  Create root fields, nested objects, arrays, primitive values, nullable fields, and reusable field names without writing a schema file by hand.

• Large Faker rule library

  Choose from person, contact, address, company, commerce, finance, date, image, color, file, Git, vehicle, airline, book, music, science, UUID, ULID, Nano ID, and text rules.

• Controlled values and ranges

  Use fixed strings, fixed numbers, custom enum values, numeric min and max values, decimal precision, string length, word count, date formatting, and coordinate output formats.

• Locale-aware mock data

  Switch locale when names, addresses, phones, companies, dates, and text should feel closer to a target market or interface language.

• Importable generator configuration

  Export your schema configuration as JSON and import it later, which is useful for repeating the same API payload, fixture shape, or demo dataset.

• JSON copy and download

  Generated data is shown as formatted JSON for review, copying, and download. The output is ready for mock APIs, seed scripts, tests, and documentation examples.

1. 1

   Set the number of rows and choose a locale for generated names, addresses, phones, companies, and text.

2. 2

   Add root fields, then click a field to edit its JSON type: object, array, string, number, integer, boolean, or null.

3. 3

   For arrays, choose the array length and item type. Object arrays can contain their own child fields.

4. 4

   Pick a generation rule for each primitive field. Use enum, fixed value, range, length, word count, date, or coordinate settings when the value needs tighter control.

5. 5

   Review the formatted JSON output, then copy it, download it, or export the configuration so the same fake data schema can be reused.

• Use nested objects for API responses that contain user profiles, shipping addresses, product metadata, billing details, or audit information.
• Use arrays for cart items, order lines, comments, permissions, tags, events, and related resources.
• Use custom enum values for status fields such as `pending`, `paid`, `cancelled`, `active`, `draft`, or `archived`.
• Use numeric ranges for prices, ports, coordinates, quantities, scores, limits, and pagination values.
• Use date formatting when the next system expects `yyyy-MM-dd`, `yyyy-MM-dd HH:mm:ss`, or another predictable date string.
• Use nullable fields to test empty states, optional profile fields, missing metadata, and partial API responses.

After generating JSON fixtures, inspect formatting, nesting, and escaped values with the JSON Formatter before pasting the payload into docs, tests, or mock APIs. If the same sample data needs to move through spreadsheets or QA handoff, prepare that flow with CSV Tools so CSV and TSV rows stay easier to review.

• Keep field names close to the API contract or database model you are testing.
• Avoid perfect data only. Include nullable fields, varied string lengths, long names, empty arrays, and mixed statuses.
• Use example email domains and synthetic finance values when data appears in screenshots, docs, or shared tickets.
• Export the configuration when the same fake data structure will be reused by multiple teammates or test runs.
• Keep generated passwords and card-like values out of real authentication or payment workflows.
• Use smaller row counts while designing the schema, then increase the count when the structure is stable.

• The generated values are fake, but formats such as credit cards, IBANs, phone numbers, and addresses may still look realistic. Do not use them to represent real people.
• This tool creates JSON data. It does not validate a formal JSON Schema document or infer a schema from an existing API automatically.
• Locale support depends on the underlying Faker dataset, so coverage can vary by field and language.
• Browser generation is practical for mock data and fixtures. Very large datasets should be generated in a script or backend job.
• Synthetic data is not anonymization. Do not mix it with real customer records and assume the result is privacy-safe.