🎲 Test Data Generator
Generate fake test data: names, emails, addresses, phones, companies, dates, UUIDs, colors. EN & JA locales. Export as CSV / JSON / SQL.
🔗 Related Tools
📖 Where people get stuck
Generate dummy names, emails, addresses, phone numbers, company names and dates, in whatever quantity you need. Everything runs in the browser. What comes out is plausibly shaped data, not a guarantee that it corresponds to nothing real: a generated name or company can coincide with a real one, and if the email domain exists, mail sent to it will actually go somewhere.
| Case | What happens | What to do |
|---|---|---|
| Test mail reaching a real domain | Even with addresses at example.com, a staging environment still configured to send will send. If any generated address lands on gmail.com or a real company domain, a stranger receives your test mail — and a spam complaint from that damages the reputation of your sending domain. |
Use the domains permanently reserved for testing by RFC 2606: example.com, example.net, example.org, and the .test, .invalid and .localhost TLDs. Nobody can register them, so a misdirected message goes nowhere. Beyond that, point SMTP at a catcher such as Mailpit or MailHog in development so there is no route out at all. |
| The data is too clean to find bugs | Generated data is uniform in length and character class, so it does not reproduce how real data misbehaves. A real name field receives one-character names, surname only, middle names, punctuation, emoji, leading and trailing spaces, and empty strings. Pass your tests on tidy data and the first thing to break in production is always an edge case. | Always mix hand-written pathological cases into the generated set. As a minimum: empty, one character, exactly the maximum length, maximum plus one, leading and trailing spaces, emoji, an embedded newline, and an SQL quote character — eight cases a generator will not produce. Keep them as fixed fixtures and run them alongside the generated rows. |
| Using production data for testing | Copying production because generating dummy data is tedious can amount to using personal data outside its stated purpose. Both GDPR and comparable national law require a basis for use beyond what was collected for. On top of that, development environments are normally less locked down than production, which makes this a realistic leak path. | When you genuinely need the production distribution, build a masked replica rather than a copy: replace names, emails and phone numbers, keep only the year of a date of birth, round addresses to the municipality. The PII masking tool helps. What matters is that the masking happens inside production and only masked data leaves — exporting the raw data first and cleaning it in the development environment defeats the purpose entirely. |
Before a generated name or company goes into a demo screen, a screenshot or a document, search for it once. Coincidences with a real person or business happen, and publishing one as your fictional customer gets you a message from them. Common name combinations and short company names collide most easily. For anything public, a name that is obviously an example, or a form like Example Inc., is the safe choice.
📖 How to Use
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Set locale and row countSelect English (US) or Japanese locale, then specify the number of rows to generate (up to 1000).
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Choose output format and fieldsChoose JSON, CSV, SQL INSERT, TSV, or Markdown table format, then check the fields you need (name, email, address, etc.).
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Generate, copy, or downloadClick Generate to create the data. Use Copy or Download to retrieve the result.
❓ Frequently Asked Questions
Is the generated data different every time?
Which databases does the SQL output support?
Is the generated data safe to use?
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