Fake Data Generator

The Fake Data Generator is an essential tool for developers who need realistic test data for development, testing, and demos. Generate up to 1000 records of fake names, emails, addresses, phone numbers, dates, and more in JSON, CSV, or SQL format—all instantly in your browser with complete privacy.

Key Features

• •Bulk Generation: Create up to 1000 records at once for comprehensive testing
• •Multiple Output Formats: Export as JSON (API mocking), CSV (spreadsheets), or SQL INSERT statements (database seeding)
• •Locale-Specific Formatting: Choose from 10+ regions for appropriate phone numbers, addresses, and name patterns
• •Drag-and-Drop Schema Builder: Visually design your data structure with 50+ field types
• •Real-Time Preview: See generated data instantly as you modify your schema
• •100% Client-Side: All data generation happens in your browser—completely private and secure

Common Use Cases

• •Seed development databases with realistic test users and data
• •Create demo accounts for presentations without using real customer information
• •Test form validation with varied inputs (long names, international characters, edge cases)
• •Generate API fixtures for frontend development before backend is ready
• •Populate UI mockups with realistic content instead of lorem ipsum
• •Load test applications with large datasets (pagination, search, filtering)

How to Use

1. 1.Drag field types from the palette into your schema (Name, Email, Address, Phone, etc.)
2. 2.Customize field names and configure options for each field
3. 3.Set the number of records to generate (1-1000)
4. 4.Choose your output format (JSON, CSV, or SQL) and locale
5. 5.Click Generate to create your fake data instantly
6. 6.Copy to clipboard or download as a file for immediate use

Whether you're seeding a database, creating demo content, or testing form validation, the Fake Data Generator provides realistic, privacy-safe test data in seconds. No server uploads, no data storage—just instant fake data generation in your browser.

Every developer needs fake data. Whether you're building a new feature and need realistic test users, creating demo content for a presentation, populating a staging database, or testing form validation with varied inputs—you need data that looks real but isn't tied to actual people. That's exactly what this tool does. It generates realistic-looking names, email addresses, physical addresses, phone numbers, dates of birth, company names, job titles, and lorem ipsum text. All data is completely fictional—generated algorithmically using common patterns from various locales. I built this because I was tired of typing 'test@test.com' and 'John Doe' repeatedly, or worse, accidentally using real-looking data that might match actual people. This tool supports bulk generation (up to 1000 records at once) in multiple formats: JSON for API mocking and frontend fixtures, CSV for importing into spreadsheets or databases, and SQL INSERT statements for direct database seeding. You can configure locales to get region-appropriate formats—US phone numbers look like (555) 123-4567, UK postcodes follow their specific pattern, Asian names use appropriate character sets and naming conventions. Perfect for development, testing, demos, and any scenario where you need realistic data without privacy concerns.

How to Use

Select the type of data you want to generate from the dropdown menu: Person (full name, email, phone), Address (street, city, state, zip/postal code, country), Company (name, industry, catch phrase), Date (birth dates, timestamps, ranges), or Lorem Ipsum text. Choose how many records to generate—anywhere from 1 to 1000. Select your preferred locale/region to get appropriately formatted data (US, UK, Germany, France, etc.—each has region-specific phone formats, address patterns, and name styles). Pick your output format: JSON (for APIs/code), CSV (for spreadsheets/imports), or SQL INSERT statements (for direct database use). Click Generate and your fake data appears instantly. Copy to clipboard or download as a file. For Person data, you get: first name, last name, full name, email (generated from name), phone number (region-formatted), and optionally username, avatar URL, or date of birth. For Address: street address, city, state/province, postal code, country. The tool ensures data consistency within each record—email addresses are derived from the generated name, addresses use real city/state combinations for that locale.

Common Use Cases

Limitations & Important Notes

Generated data is fictional but follows realistic patterns—don't use in production or assume it represents real demographic distributions. Names and addresses are randomly combined and may not correspond to actual locations (e.g., a California city might be paired with a New York zip code in some edge cases—though the tool tries to maintain regional consistency). Email addresses look real but are generated patterns (usually [firstname].[lastname]@example.com)—they're not actual working email addresses, don't try to send mail to them. Phone numbers follow format patterns but aren't necessarily valid assignable numbers. The tool generates up to 1000 records at once; for larger datasets, use command-line tools like Faker.js in Node.js scripts for better performance. Generated data is random per session—you can't recreate the exact same dataset unless you use a seeded random generator (not available in this browser version). Locale support covers major regions but may not include every country's specific format quirks. Don't use generated data for security testing (passwords, if generated, are weak), compliance testing (not GDPR/privacy-representative), or performance benchmarks (data distribution doesn't match production reality). The tool is for development and testing convenience, not for creating datasets for research or analysis.

Tips & Best Practices

**Development workflows**: Use JSON output for API mocking or frontend fixtures, CSV for spreadsheet imports or database seeders, SQL for direct database population. Always add 'test_' prefix or special markers to fake emails (test_john@example.com) to prevent accidental real-user notifications. Common scenarios: testing pagination with 100+ records, validating email uniqueness constraints, stress-testing search with varied names/addresses, and creating privacy-safe screenshots for documentation. Remember: generated data is random but deterministic per session—refresh for new data. Don't use for security testing (passwords are weak) or performance benchmarks (data distribution isn't production-realistic). For large datasets (10k+ records), use command-line Faker.js with custom scripts for better performance and control.