Fake Data Generator

Features

• Twelve field types covering the common cases
• Four output formats including ready-to-run SQL
• Four naming styles for realistic international data
• Up to 500 records at a time
• Internally consistent, emails match the generated names

How to use it

1. Toggle the fields you need.
2. Set how many records to generate.
3. Pick an output format.
4. Copy or download the result.

Why you should not test with real data

Copying a production database into a staging environment is common and is a serious problem. Staging environments typically have weaker access controls, less monitoring, wider developer access and looser backup hygiene than production. Under GDPR, personal data in a test system is still personal data, subject to the same lawful-basis, retention and breach-notification obligations, and a staging leak is a reportable breach exactly like a production one.

Generated data avoids all of that, and tends to produce better tests. Real data clusters around the ordinary: mostly short names, mostly one address format, mostly the same handful of domains. Synthetic data can be deliberately awkward, apostrophes in surnames, very long strings, unusual characters, which is what actually finds the bugs.

If you must derive test data from production, anonymisation is harder than replacing names with X. Pseudonymised data is still personal data under GDPR if it can be re-linked, and re-identification from a handful of quasi-identifiers like postcode, date of birth and sex is well documented. Generating fresh data sidesteps the question. Everything produced here is fictional, any resemblance to a real person is coincidental, and the addresses and card-style numbers are not valid.