Softonic

Fake Data Generator

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Generate rows of fake-but-realistic user records in one click: ID, full name, email, phone and city. Output is a CSV with a header row you can paste into a spreadsheet, or JSON for API mocks. Useful for seeding a dev database, populating a dashboard preview, writing integration tests or stress-testing a CSV importer without exposing any real user data.

How to generate a fake dataset

  1. 1

    Pick the record count

    Choose how many rows you need: typical runs are 10, 50 or 200 records.

  2. 2

    Choose the format

    CSV for spreadsheets and SQL imports, JSON for API fixtures and front-end mocks.

  3. 3

    Generate

    The generator randomly assembles names, emails, phone numbers and cities into records with sequential IDs.

  4. 4

    Copy or download

    Copy the output to paste into a tool, or save as a `.csv` or `.json` file to feed your test harness.

What the generator produces

The goal is believable shape, not high-fidelity statistics. Names come from a mixed pool so they read as human rather than obviously templated.

Fields per record

Field Shape Example
id Sequential integer starting at 1 17
name First + last from mixed pools Morgan Lopez
email Normalized name + id @example.com morgan.lopez17@example.com
phone North American format, fake area +1-555-412-8203
city European capital from a short list Berlin

Why @example.com and 555-

These are reserved for documentation and fiction by RFC 2606 and NANP. Using them guarantees the generated addresses and phone numbers never collide with real people, so your test data cannot accidentally hit a real inbox or dial a stranger at 3 AM.

Sensible use cases

  • Seeding a staging database with enough rows that list views, search and pagination behave realistically.
  • Load-testing a CSV importer, generate 200 records, then multiply to 200 000 in a spreadsheet before upload.
  • Mocking an API during front-end development, swapping the JSON output into a fixtures file.
  • Design tokens for a dashboard preview so stakeholders see a busy UI instead of an empty state.

What it is not

Not a faker library for every locale (use Faker, @faker-js/faker or Chance for that). Not a source of statistically realistic demographics, distributions here are uniform and small-pool. If you need locale-correct names, addresses with valid postcodes, or industry-specific fields, reach for a full library.

Frequently Asked Questions

Yes. Emails end in @example.com, a domain reserved by RFC 2606 that no one owns. Phone numbers use the 555- prefix reserved in the North American numbering plan for fiction. Neither will ever reach a real person.

Every run uses fresh randomness, so results are different each time. If your tests need deterministic data, generate once, save the file to your repo and treat it as a fixture.

The output is capped at 200 per generation to keep the page snappy. For larger volumes, paste the CSV into a spreadsheet and duplicate rows, or run a dedicated library locally.

The records are created during the request, sent back to you, and not retained in storage or logs.

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