Mock Data Generator

Generate Realistic Fake Test Data for Development

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Why Use a Mock Data Generator?

Developers need realistic test data for database seeding, API mocking, UI prototyping, and QA testing. AI chatbots hallucinate inconsistent data — they generate wrong formats, mismatched fields, and unrealistic values. This tool uses deterministic JavaScript with curated data pools to produce consistent, realistic fake data every time.

Available Data Presets

Users

name, email, phone, username, address, job title, company, avatar URL, date joined, status

Products

name, description, price, category, SKU, stock, rating, image URL, created date, featured

Companies

name, industry, employees, revenue, founded, website, address, phone, CEO, description

Addresses

street, city, state, zip code, country, latitude, longitude, timezone, phone, type

Blog Posts

title, slug, excerpt, content, author, category, tags, published date, read time, status

Transactions

id, amount, currency, status, customer email, payment method, product, date, quantity, total

Why AI Cannot Generate Reliable Test Data

  • Inconsistent formats: LLMs produce different formats for the same field type across records — one phone number might be (555) 123-4567, another 555.123.4567, another just "unknown"
  • Schema violation: AI often includes fields you didn't ask for, omits required fields, or creates mismatched data types (strings in number fields)
  • Unrealistic values: Language models hallucinate implausible data like "[email protected]" for every record, birthdays in the 1800s, or negative prices
  • No referential integrity: Cross-field relationships are broken — a user's country might not match their zip code format, or a transaction references a non-existent product
  • Non-reproducible: AI generates different data every query, making debugging impossible and automated test suites unreliable

This tool generates deterministic, schema-consistent fake data with realistic distributions. Every field type produces valid, properly formatted output every time.

Export Format Options

  • JSON: Array of objects. Perfect for API mocking, fixture files, and importing into JavaScript/TypeScript projects
  • CSV: Comma-separated with header row. Compatible with Excel, Google Sheets, database import wizards, and data processing pipelines
  • SQL INSERT: Multi-value INSERT statements. Ready to run against MySQL, PostgreSQL, SQLite. Just set the table name and execute

Common Use Cases

  • Database seeding: Populate development/staging databases with realistic data without exposing real user information
  • API mocking: Generate consistent mock API responses while frontend teams develop independently of backend
  • UI prototyping: Fill dashboards, tables, and forms with believable data to evaluate layouts and user flows
  • Automated testing: Create reproducible test fixtures for unit, integration, and end-to-end tests
  • Load testing: Generate large datasets to benchmark query performance and database indexing strategies
  • Demo environments: Provision sandbox accounts with realistic-looking data without compliance risk

Frequently Asked Questions

Is this tool really free? Any limits?

Yes, completely free with no limits. Generate up to 500 rows per batch, as many times as you want. No signup, no account, no hidden costs. All data generation happens in your browser.

Is my data private?

100% private. All data generation occurs in your browser using client-side JavaScript. No schemas, field names, or generated data are sent to any server. Open your browser's Network tab to verify — zero outbound requests containing your data.

How many rows can I generate?

Up to 500 rows per batch. For most testing and prototyping, 10-50 rows is sufficient. For larger datasets, generate multiple batches and concatenate the output. The practical limit is your browser's memory.

Can I create custom field schemas?

Currently this tool offers 6 pre-built presets covering the most common testing needs (users, products, companies, addresses, blog posts, transactions). For fully custom schemas with unlimited field types, we recommend using the Faker.js library directly in your project.

Does this use Faker.js?

The tool uses its own curated data pools for first/last names, company names, product names, addresses, and other field types — no external library dependency. This ensures faster loading, zero CDN failures, and consistent output without relying on third-party packages.

Can I reproduce the same dataset?

Each generation produces new random values. For fully reproducible data (same seed = same output every time), use a dedicated library like @faker-js/faker in your project with a fixed seed value.

How is this different from ChatGPT generating test data?

AI chatbots hallucinate inconsistent formats, violate schemas, produce unrealistic values, and generate different output each time. This tool produces deterministic, schema-consistent data with proper formatting across all records. Every field type is guaranteed valid, every format is consistent, and the output is ready to use immediately.

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