Is the generated data real?

No. All names, emails, addresses, and phone numbers are randomly assembled and completely fake. Any resemblance to real persons or actual email addresses is purely coincidental. They are designed strictly for testing and design purposes.

Can I generate thousands of rows of data?

To ensure optimal browser performance, this client-side tool is currently limited to generating 500 records at a time. If you need millions of rows, we recommend using a backend scripting library like Faker.js or Python Faker.

What export formats are supported?

You can export your generated data as a JSON array (perfect for mocking REST APIs), a CSV file (ideal for database imports and Excel), or as raw SQL insert statements to execute directly against your database.

Are the email addresses valid?

The generated email addresses are structurally valid (e.g., name@domain.com) so they will pass standard regex validation in your forms, but the domains and inboxes do not actually exist.

Does the tool guarantee unique values?

The tool uses randomization algorithms that make collisions highly unlikely, especially for fields like UUIDs and IPv6 addresses. However, for smaller dictionary fields (like first names or cities), duplicate values may occasionally appear across a large dataset of 500 rows, mirroring real-world data distribution.

Can I save my schema for later?

Currently, the schema is held in your active browser session. If you refresh or close the page, the schema will reset to the default configuration. We recommend exporting your data once you are satisfied with the structure.

Mock Data Generator – Free Fake & Dummy Data Creator

The Mock Data Generator is a developer-focused utility for instantly generating realistic fake data. Whether you are seeding a test database, populating a user interface mockup, or writing unit tests, you need data that looks like the real thing.

What Is the Mock Data Generator?

The Mock Data Generator is a flexible tool that allows you to construct custom data schemas visually and populate them with random, realistic dummy data. Instead of hardcoding generic placeholder text like "John Doe" or "test@test.com" into your application, this tool generates diverse names, valid email formats, realistic addresses, hex colors, and timestamps.

By generating data that mimics the shape and variance of real-world information, you can build more robust UIs that properly handle long strings, unexpected characters, and varied formatting.

How to Use the Mock Data Generator

Start by defining your schema. The tool provides a default set of fields (ID, Name, Email, Company), but you can easily add, remove, or rename fields to match your specific database table or API payload.

For each field, select a data type from the dropdown menu. We support over a dozen types including UUIDs, IPv6 addresses, URLs, and Prices. As you build your schema, a live 5-row preview automatically updates in real-time so you can verify the data format.

Once your schema is set, choose how many rows you want to generate (up to 500) and select your export format.

Common Use Cases

Frontend developers use mock data to populate data tables and dashboard charts before the backend API is ready. Backend engineers use it to seed test databases with thousands of rows to test query performance and pagination logic. UI/UX designers use it to ensure their layouts don't break when a user has a highly unusual name or a long email address.

Privacy and Browser-Based Processing

All data generation happens entirely locally in your browser using mathematically random algorithms and predefined dictionaries. Your custom schemas are never sent to a server, and the generated files are created directly in your browser memory, ensuring your workflow remains incredibly fast and completely private.

Mock Data Generator

Data Schema