Is this real people's data?

No — every value is randomly generated from generic word lists in your browser. Email addresses use example.com/.org/.net, which are reserved for testing and can never reach a real inbox.

Is the data sent to a server?

No — generation happens entirely in your browser, in every output format.

Can I get the same data again later?

Yes — reuse the same seed number with the same columns and row count to reproduce identical output, in any format.

Is there a row limit?

Yes, 5000 rows per generation, to keep the tool responsive in your browser.

What SQL dialect do the INSERT statements target?

A generic, widely-portable syntax with backtick-quoted identifiers and single-quoted strings — compatible with MySQL and SQLite, and easy to adapt for other databases.

Fake Data Generator

Generate realistic-looking, entirely synthetic rows of test data to seed a database, mock an API response, or fill a demo — in the output format your project already uses.

100% synthetic data, generated in your browser. No real people, no external API calls, nothing sent anywhere. For testing and development only — do not use it to impersonate real people or create fraudulent accounts.

Columns

Min

FromTo

Rows (max 5000)Seed (for reproducible output)

Format

Preview (first 10 of 20 rows)

id	name	email	signupDate
1	Wei Chraibi	ava.haddad50@example.org	2023-02-23
2	Ines Ferreira	julia.johnson94@example.net	2024-12-08
3	Sofia Fassi	ava.chraibi77@example.net	2024-04-16
4	Gabriel Kowalski	mason.johnson7@example.com	2023-05-31
5	Ines Idrissi	maya.elamrani62@example.net	2023-09-11
6	Nora Bennani	chloe.haddad4@example.org	2024-05-27
7	Priya Bennani	mia.nguyen58@example.net	2023-12-03
8	Adam Fassi	lucas.novak30@example.com	2024-09-16
9	Noah Nguyen	lina.bennani18@example.net	2023-11-08
10	Hugo Smith	sara.martin97@example.net	2024-07-14

Processing happens locally in your browser. Nothing you enter is uploaded.

How It Works

Add one or more columns and pick a type for each (name, email, integer, date…), set the number of rows, then choose an output format — JSON, CSV, XML, a Python list of dicts, SQL INSERT statements, or YAML — before downloading or copying the result. Use the same seed to reproduce the exact same data again later.

Example

A developer needs 200 fake user rows (name, email, signup date) as SQL INSERT statements to seed a staging database before running an import test.

Frequently Asked Questions

Is this real people's data?: No — every value is randomly generated from generic word lists in your browser. Email addresses use example.com/.org/.net, which are reserved for testing and can never reach a real inbox.
Is the data sent to a server?: No — generation happens entirely in your browser, in every output format.
Can I get the same data again later?: Yes — reuse the same seed number with the same columns and row count to reproduce identical output, in any format.
Is there a row limit?: Yes, 5000 rows per generation, to keep the tool responsive in your browser.
What SQL dialect do the INSERT statements target?: A generic, widely-portable syntax with backtick-quoted identifiers and single-quoted strings — compatible with MySQL and SQLite, and easy to adapt for other databases.