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Free Mock Data Generator — Fake JSON Data from Schema, Fields & Samples | DevFlow

Mock Data Generator

Generate realistic fake JSON data from schemas, field templates, or sample data for testing and prototyping.

Free online mock data generator for developers, QA testers, and designers. Generate realistic fake data from JSON Schema definitions, custom field templates, or by inferring types from a sample JSON object. Choose from 30+ field types including first names, last names, emails, phone numbers, street addresses, cities, countries, UUIDs, dates, timestamps, company names, job titles, URLs, IP addresses, credit card numbers, and more. Support for 9 locales including English, German, French, Spanish, Italian, Portuguese, Japanese, Korean, and Chinese for region-specific data. Generate 1 to 10,000 records with optional null values and reproducible output via seed. Built-in presets for Users, Products, Orders, and Posts get you started in one click. Output is formatted JSON ready for test fixtures, API mocking, database seeding, and UI prototyping. Runs entirely in your browser — no data is ever sent to a server. Free REST API available for programmatic access.

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How to Mock Data Generator Online

  1. Choose your input mode — Field Builder, JSON Schema, or Infer from Sample — using the mode selector on the left panel.

  2. In Field Builder mode, add fields by name and select a data type from 30+ options including names, emails, addresses, UUIDs, dates, and more. Or load a preset (Users, Products, Orders, Posts) for instant setup.

  3. In JSON Schema mode, paste a JSON Schema definition and the tool automatically maps schema types and format hints to realistic fake data.

  4. In Infer from Sample mode, paste a sample JSON object or array and the tool infers field types from the data patterns — strings that look like emails become emails, UUID-shaped strings become UUIDs, and so on.

  5. Set the record count (1–10,000) using the slider or input field. Choose a locale for region-specific names, addresses, and phone numbers.

  6. Optionally configure a seed value for reproducible output across runs, and toggle null values with adjustable probability.

  7. Click Generate or press ⌘↵ to create your mock data. Output appears as formatted JSON instantly.

  8. Copy the generated JSON with ⌘⇧C, download as a .json file with ⌘⇧D, or share your configuration via URL parameters.

Mock Data Generator Features

  • 3 input modes: Field Builder for explicit control, JSON Schema for schema-driven generation, and Infer from Sample to auto-detect field types from example data.

  • 30+ realistic data types: first name, last name, full name, email, phone number, street address, city, country, company name, job title, URL, IPv4, IPv6, UUID, date, timestamp, number, float, boolean, word, sentence, paragraph, color, hex color, emoji, avatar URL, password, credit card number, and bank account IBAN.

  • 9 locale support: generate region-specific data for English (US), German, French, Spanish, Italian, Portuguese (Brazil), Japanese, Korean, and Chinese (Simplified) — with localized names, addresses, and phone formats.

  • 4 built-in presets: instantly load field templates for Users, Products, Orders, and Posts to get started in one click.

  • Reproducible output: set a seed value to generate identical data across multiple runs — ideal for consistent test fixtures and database seeding.

  • Nullable field support: optionally include null values in your output with configurable probability (0–50%) to test null-handling logic.

  • JSON Schema intelligence: automatically maps JSON Schema types and format hints (email, uri, uuid, date-time, ipv4, ipv6) to appropriate faker types, and handles enum values.

  • Smart type inference from sample JSON: detects email patterns, UUID shapes, URL prefixes, date formats, numeric types, and more from your sample data.

  • Bulk generation: create up to 10,000 records in a single run with real-time performance stats showing record count, field count, output size, and generation time.

  • Shareable URLs: all settings are encoded in URL parameters so you can bookmark or share your exact configuration with teammates.

  • Works entirely in your browser — your schemas, samples, and generated data never leave your machine. No signup required.

  • Keyboard shortcuts for power users: ⌘↵ to generate, ⌘⇧C to copy, ⌘⇧D to download, ⌘⇧K to clear.

  • REST API available at /api/tools/mock-data-generator for programmatic access — supports GET with query parameters and POST with JSON body.

Frequently Asked Questions

What is a mock data generator?
A mock data generator creates realistic fake data — names, emails, addresses, phone numbers, dates, and more — for use in software development, testing, and prototyping. It replaces the need to use real user data or manually create test records, providing realistic datasets that look like production data without any privacy concerns.
Is this mock data generator free?
Yes, DevFlow Mock Data Generator is completely free with no usage limits. All data generation runs directly in your browser — no account, subscription, or signup required.
Is my data safe?
All data generation happens entirely in your browser using JavaScript. Your JSON Schemas, sample data, and generated output are never transmitted to any server or stored anywhere outside your browser. No real personal data is used — all output is synthetically generated.
How do I generate mock data from a JSON Schema?
Switch to JSON Schema mode and paste your JSON Schema definition. The tool automatically parses property types and format hints (such as format: 'email', format: 'uuid', format: 'uri', format: 'date-time') to generate matching fake data. Supported types include string, number, integer, boolean, and enum values.
How does the 'Infer from Sample' mode work?
Paste a JSON object or array into the Sample JSON textarea. The tool analyzes each field value to detect its type — email-shaped strings become email fields, UUID-shaped strings become UUID fields, ISO date strings become date fields, and so on. It then uses these inferred types to generate realistic data matching the structure of your sample.
What locales are supported?
The tool supports 9 locales: English (US), German, French, Spanish, Italian, Portuguese (Brazil), Japanese, Korean, and Chinese (Simplified). Each locale generates region-appropriate names, addresses, company names, and phone number formats. Select your locale from the dropdown in the options panel.
What is the seed option for?
The seed value ensures reproducible output. When you set a seed, the generator produces identical data every time you use that same seed with the same configuration. This is useful for creating consistent test fixtures, sharing reproducible datasets with teammates, or regression testing where you need the same data across test runs.
How many records can I generate?
You can generate between 1 and 10,000 records in a single run. The tool shows real-time statistics including record count, field count, output size, and generation time so you can monitor performance. For most use cases, a few hundred records provide excellent test coverage.
What data types are available?
The Field Builder offers 30+ data types organized into categories: Person (first name, last name, full name, email, phone, avatar, password), Address (street address, city, country), Company (company name, job title), Web (URL, IPv4, IPv6), ID (UUID), DateTime (date, timestamp), Numbers (integer, float), Text (word, sentence, paragraph), Color (color name, hex color, emoji), and Finance (credit card number, bank account IBAN).
Can I include null values in the output?
Yes. Enable the 'Include nulls' option and set the null probability (0–50%). The generator will randomly replace field values with null at the specified rate. This is useful for testing null-handling code, optional field behavior, and API error handling in your application.
What is the difference between mock data and synthetic data?
Mock data is randomly generated fake data designed to look realistic for testing and prototyping — it has no statistical relationship to real data. Synthetic data, by contrast, is generated to preserve the statistical properties and patterns of real datasets. Mock data is ideal for development, unit testing, and demos, while synthetic data is better for analytics, machine learning, and compliance-sensitive environments.
Can I use the generated data in my tests?
Yes. All generated data is entirely synthetic and contains no real personal information. You can freely use it in unit tests, integration tests, database seeding, API mocking, UI prototyping, and demo environments. The output is formatted as JSON and can be directly imported into test fixtures or mock servers.
Does this tool have an API?
Yes. The Mock Data Generator is available as a free REST API at /api/tools/mock-data-generator. Supports GET with query parameters and POST with JSON body. Accepts mode, count, locale, seed, fields array, schema string, and sample string. Returns generated data and statistics in JSON format.
Does it work offline?
Yes. All data generation logic using @faker-js/faker is bundled with the page as JavaScript. Once the page has loaded, the Mock Data Generator works without an internet connection.

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