What makes this different from a random-data library?
Seeding a database, filling a staging environment or building a fixture all need data that looks real without being real. This generates it from 40 field types across seven categories, and does so deterministically: the same columns and row count produce the same values every time, so a fixture stays stable across runs instead of churning your diffs. Values within a row are also coherent — the email matches the name rather than being drawn independently.
How many field types are available?
40, grouped into seven categories: personal, address and geo, internet, finance, business, commerce, and developer
Is the data random?
No, it is deterministic. The same columns and row count produce the same values every time, which is what makes it usable as a committed fixture. Regenerating with a new salt gives you a fresh set
How many rows can I generate?
Up to 10,000. The default is 25
What formats can I export?
CSV, JSON, tab-delimited and Excel
Is the generated data safe to use in a shared environment?
It is synthetic — assembled from name, place and product word lists rather than derived from real people — so it carries none of the privacy obligations real customer data would