Dummy Data Generator
Generate realistic-looking fake test data — names, emails, addresses, phone numbers, and more — as JSON or CSV, in whatever quantity you need for testing or demos.
Testing with data like “Test User 1” and “asdf@asdf.com” works, but it’s a poor stand-in for how a UI or export actually behaves with realistic-length names, varied international addresses, or properly formatted phone numbers — edge cases that only show up with data that looks like it could be real. This tool generates data that’s fake but structurally realistic, without using any actual real person’s information.
Choose which fields you need (name, email, address, phone, company, date, and others), set how many records to generate, and pick your output format — JSON or CSV. Each field is generated independently and randomly, so you get varied, non-repetitive results rather than the same handful of placeholder values cycling repeatedly. Download the result directly or copy it for pasting into a test database, spreadsheet, or seed script.
Frequently asked questions
Is any of the generated data based on real people?
No — every field is generated from randomized combinations of common name components, address formats, and similar patterns, producing data that looks plausible without corresponding to any real individual.
Can I generate data matching a specific country’s formatting conventions, like phone numbers or postal codes?
Yes, a region selector adjusts formatting for names, phone numbers, and addresses to match common conventions for the selected country or region, rather than defaulting to one fixed format regardless of locale.
How large a dataset can I generate at once?
Comfortably into the thousands of records for typical use, generated locally without a server round-trip — for genuinely large-scale seed data (tens of thousands of rows or more), a dedicated database seeding script is usually a better fit than a browser-based tool.
Once I have the JSON output, can I turn it into SQL INSERT statements directly?
Yes, export as CSV first, then run that through the CSV → INSERT SQL tool to get ready-to-run insert statements for your database.