Zellio.io

Mock Data Generator

Generate realistic fake names, emails and rows as JSON or CSV.

Runs locallyWorks offlineShare link carries settings, never your data
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Frequently asked questions

How many rows can I generate?

Up to 1,000 rows at a time, across as many fields as you like. Click Regenerate for a fresh randomised set.

What export formats are supported?

JSON (with real numbers and booleans) and CSV (with proper quoting), both of which you can copy or download.

Is the generated data based on real people?

No. All values are randomly assembled from generic sample lists, so the records are fictitious and safe to use in tests and demos. The generator combines random first/last names, fake emails (e.g., john.smith@example.test), and synthesized addresses to create realistic-looking but entirely made-up records. This ensures test data never accidentally matches real people or contains real personally identifiable information. The synthetic data is ideal for development, QA testing, and design mockups where authentic-looking data matters but privacy is paramount.

Pro tips

  • Generate once and commit the output as a fixture. Data that changes on every run turns a failing test into a mystery, because the input nobody recorded is gone.
  • Include at least one long name and one long email in any set you use for layout review — the widest row is the one that reveals the overflow.
  • Export JSON when the consumer is code and CSV when it is a spreadsheet or an import wizard; CSV loses the distinction between the number 42 and the string "42".
  • Use UUID fields for anything that will become a primary key, so you can paste the same set into two systems and still join them afterwards.
  • Regenerate with the same schema to sanity-check that your rendering is data-driven and not accidentally keyed to one specific value.

About Mock Data Generator

Build a schema field by field — person details, contact information, identifiers and dates — then produce up to 1,000 rows in either export format. Plausible-looking values make a prototype far easier to judge than rows of test1, test2, test3.

The mock data generator builds realistic sample records for testing, prototyping and demos. Define a schema field by field — full names, emails, phone numbers, cities, companies, UUIDs, dates, booleans and more — set how many rows you need, and generate up to 1,000 records instantly. Export the result as ready-to-use JSON or CSV.

It saves hand-typing fixtures or wiring up a database just to see how a table, form or API renders with real-looking data. Varied name and address lengths also expose layout bugs that a column of identical placeholder strings hides.

Realistic values matter because they break things that placeholder strings never touch. A surname with an apostrophe finds the query that was never parameterised; a three-line address finds the table cell with a fixed height; an email at a long domain finds the column that truncates without an ellipsis. Rows of test1, test2, test3 are uniform in exactly the ways real data is not, so they pass every layout and escaping bug straight through to production.

Volume is the other axis worth exercising. Ten rows tell you a table renders; a thousand tell you whether pagination, sorting and virtual scrolling hold up, and whether that per-row lookup you wrote is quietly running a thousand times.

Dates deserve deliberate attention when generating a set. Ranges that never cross a month or year boundary hide off-by-one errors in grouping and filtering, and a set where every record falls in the recent past will not exercise whatever your interface does with a very old or future-dated row. Generating across a wide span costs nothing and finds the formatting and sorting bugs that a tidy fortnight of sample data never will.

Common use cases

  • Front-end developers filling a table, list or dashboard during design review, so stakeholders judge the layout instead of squinting past lorem ipsum.
  • QA engineers building fixture files for automated tests that need plausible names, emails and dates without touching a production export.
  • Backend developers load-testing an import endpoint with a thousand well-formed CSV rows before letting a customer upload their own.
  • Designers populating a prototype in Figma or a static mockup with content of realistic length rather than uniformly short strings.
  • Teams demoing a product to a client who should see something that looks like their own data, not obvious filler.
How it comparesMockaroo offers more field types and formula support but caps free generation and requires an account past a certain size; Faker.js gives you the same variety in code at the cost of writing and running a script. This sits between them: no signup and no script, at the price of a smaller field library.