randarium
Datasets

Fake User Dataset Generator

Create reproducible synthetic user datasets with names, contact fields, locations, dates, plans, and statuses. This data is fictional and must not be used as real identity data.

Also known as: mock users · dummy users · fake csv data

seeded · synthetic data

Output

No output yet — set your options and hit .
About this tool, tips & examples

What it does

The Fake User Dataset Generator produces reproducible, clearly synthetic user tables — names, contact fields on the reserved example.test domain, locations, signup dates in your chosen range, plans, and statuses — up to 10,000 rows per run. Control the plan and status distributions to shape realistic mixes (mostly-free with some paid, mostly-active with some churned), add nulls for import testing, and regenerate the identical dataset from the seed. This is the classic “fake CSV data” tool.

Common use cases

  • UI prototypes — user lists, admin panels, and search results with believable variety.
  • Import testing — CSV/NDJSON files with a controlled null rate for validating parsers, required fields, and error reporting.
  • Analytics examples — signups-over-time, plan mix, and churn charts driven by the distribution controls.
  • Database seeding — a stable users table for dev and staging, pinned by the seed.

Settings

  • Rows — 1 to 10,000 users.
  • Country — shapes names and locations.
  • Plan / Status distribution — the mix of plans and account states.
  • Start / End date — the signup-date window.
  • Null rate — 0 to 1; randomly blanks nullable fields.
  • Seed — identical seed + settings = identical rows.

Privacy note

Rows are generated locally in your browser and never uploaded. No real identities are involved — names come from small built-in lists and every email uses example.test, a reserved domain that can never receive mail. Don’t contact, bill, or make decisions about anyone in this data; there is no one.

FAQ

Is this real or anonymized personal data? Neither — it’s synthesized from scratch, so there’s nothing to re-identify. That’s what makes it safe for screenshots and shared environments.

Why example.test emails? It’s a reserved domain: mail can’t be delivered, so an accidental “send welcome email to all users” in a test environment harms no one.

How do I test messy imports? Raise the null rate and re-import — your validation, defaults, and error UI meet realistic gaps. Same seed = same gaps every run.