Fake Data Generator
Generate realistic fake JSON data for testing and prototyping. Build a custom schema with names, emails, UUIDs, dates, and more — then generate as many rows as you need.
How to use
- 1 Click "Add field" and select a data type for each field: name, email, UUID, date, phone, etc.
- 2 Set the number of rows to generate (up to 50).
- 3 Click Generate — a JSON array of realistic fake records appears instantly.
- 4 Click Copy or Download JSON to use the data in your tests, mockups, or database seeds.
Key features
- Build a custom schema with 20+ field types: name, email, UUID, phone, address, date, and more
- Generate up to 50 rows of realistic fake data per click
- Download as a JSON file or copy the raw array
- Ideal for seeding test databases, API mocks, and UI prototypes
What is a Fake Data Generator?
A fake data generator (also called a mock data generator) creates realistic-looking but entirely synthetic data for testing, prototyping, and development. Instead of manually writing JSON or using production data (which may contain sensitive information), you define a schema and the tool generates as many rows as you need — with names, emails, UUIDs, phone numbers, addresses, and 20+ data types built in.
This is especially useful in test-driven development, CI/CD pipelines, and demo environments where realistic data is needed but real user data cannot be used due to privacy regulations like GDPR or HIPAA. All generation happens locally in your browser — no data is uploaded to any server.
Common Use Cases
Unit and integration tests
Generate realistic test data for your test suites — no more hardcoded fixtures or brittle test data.
Frontend mock APIs
Build and test your UI against realistic JSON responses while the backend is still under development.
Database seeding
Populate development and staging databases with hundreds of realistic records to catch edge cases early.
User acceptance testing
Provide QA teams and stakeholders with realistic demo environments that look and feel like production.
Load and performance testing
Generate varied datasets to test how your application behaves under realistic data volumes and distribution.
AI / ML training data
Create synthetic datasets for training machine learning models when real data is scarce or privacy-protected.
Supported Data Types
20+ field types you can mix in any schema.
| Type | Description | Example output |
|---|---|---|
| uuid | Random UUID v4 identifier | 550e8400-e29b-41d4-a716-446655440000 |
| first_name | Random first name | Alice |
| last_name | Random last name | Johnson |
| full_name | First + last name combined | Carlos Garcia |
| Realistic email address | alice.smith42@gmail.com | |
| phone | US-format phone number | +1-555-123-4567 |
| company | Fictional company name | DataFlow |
| address | Street address | 742 Main St |
| city | Major world city | Tokyo |
| country | Country name | Canada |
| zip_code | 5-digit postal code | 90210 |
| url | Company website URL | https://www.acmecorp.com |
| avatar_url | Random avatar image URL | https://i.pravatar.cc/150?u=... |
| date | Random date (YYYY-MM-DD) | 2025-09-14 |
| timestamp | ISO 8601 timestamp | 2025-09-14T08:30:00.000Z |
| boolean | Random true/false | true |
| number | Random integer in range | 742 |
| text | Lorem ipsum paragraph | Lorem ipsum dolor sit amet... |
| color | Random hex color | #a3f07b |
| ip_address | Random IPv4 address | 192.168.1.42 |
| user_agent | Browser user agent string | Mozilla/5.0 (Windows NT 10.0;... |
Frequently Asked Questions
What is fake data used for?
Fake data is primarily used in software development and testing — populating databases with realistic records, creating mock API responses for frontend development, generating test fixtures for unit and integration tests, and building demos or prototypes without exposing real user information. It's also commonly used for load testing and performance benchmarking.
Is fake data generation safe?
Yes. This fake data generator runs 100% in your browser with no server uploads. All generated data stays on your machine. The names, addresses, and other data points are pulled from a built-in pool of synthetic values — they are not real people's information. However, you should still be careful not to confuse generated data with real production data in your systems.
Can I generate more than JSON?
Currently this generator outputs JSON, which is the most common format for APIs, configuration files, and database seeding scripts. You can copy the output directly or download it as a .json file. For other formats like CSV, SQL inserts, or XML, you can use a converter tool like CSV ↔ JSON or JSON ↔ XML available on this site.
How many rows can I generate at once?
You can generate up to 50 rows at a time. This limit keeps the generation fast and prevents browser performance issues, especially with complex schemas. If you need larger datasets, you can generate multiple batches and merge them, or use a dedicated CLI tool like Faker.js or Mockaroo for bulk generation.
How is this different from Faker.js?
Faker.js is a popular Node.js library for generating fake data programmatically. This tool provides the same kind of data generation but through a visual schema builder — no coding required. You add fields, pick types, and generate data instantly. Internally it uses similar principles, but the output is ready-to-use JSON you can copy or download directly.