Generate
Fake Data Generator for Testing & Demos
Generate realistic fake names, emails, addresses and more as JSON or CSV.
Runs entirely in your browser — nothing you paste is uploaded or stored.
What is fake data generator?
Testing, prototyping and demos all need data that looks real without being real — a spreadsheet of made-up customers, a JSON fixture for an API test, a CSV to try an import flow against. This tool builds rows of fake data from a set of fields you choose (name, email, address, company, UUID, a custom-range number, and more), and exports them as JSON or CSV. Every field within a row is generated together, so a name and its matching email stay consistent with each other, and an optional seed lets the exact same dataset be regenerated again later — useful for a reproducible test fixture, not just a one-off sample.
When to use it
- Generating realistic-looking sample rows for a UI mockup or demo, without using anyone's real data.
- Building a JSON or CSV test fixture for an API test suite or a database seed script.
- Trying out a CSV import flow with a range of column types (text, numbers, dates, booleans) before pointing it at real data.
- Producing a batch of test UUIDs, emails, or IDs for load-testing or QA scenarios.
How to use this tool
- Add the fields you need and give each one a column name — pick from names, contact info, addresses, company info, or generic types like integer, boolean, date and UUID.
- Set a numeric range for integer/float fields, and choose how many rows to generate.
- Choose JSON or CSV as the output format.
- Copy the result, download it as a file, or pin the seed shown below the output to regenerate this exact dataset again later.
Example
Three rows with a name, email and job title.
Input
Fields: Full name, Email, Job title — 3 rows, JSONOutput
[
{ "Full name": "Ada Chen", "Email": "ada.chen@example.com", "Job title": "Data Analyst" },
{ "Full name": "Noah Silva", "Email": "noah.silva@testmail.dev", "Job title": "UX Designer" },
{ "Full name": "Priya Kim", "Email": "priya.kim@mail.example", "Job title": "Product Manager" }
]Each row's name and email are generated together, so the email always matches the name in that same row rather than being drawn independently.
Why fields in the same row are generated together
A row where “Full name” says “Ada Chen” but “Email” says “noah.silva@example.com” looks obviously fake in a way that undermines the point of a realistic-looking fixture. This tool generates a person’s name once per row, then derives every name-based field (full name, first name, last name, email, username) from that same value, so a row reads as one consistent person rather than several independently rolled fields glued together.
The UUID field matches the standalone generator’s format
The UUID field produces a structurally valid v4 UUID, the same format the dedicated UUID Generator produces — drawn from this tool’s own seeded random source rather than the browser’s CSPRNG, so a pinned seed reproduces the exact same value across runs instead of a fresh random one each time.
Frequently asked questions
Is this data actually real?
No — every value is drawn from curated fake name, address, company and word lists, or generated numerically (UUIDs, numbers, dates, booleans). Email addresses use non-routable placeholder domains rather than real ones. Any resemblance to a real person, company or address is coincidental, the same way it would be with any fake-data tool.
What does the seed actually do?
It makes the "random" output reproducible. With no seed, a fresh random batch is drawn every time and shown below the output so it can be reused later. Entering that same number back into the Seed field regenerates byte-for-byte identical data — useful for a test fixture that needs to be the same across CI runs, or for sharing a specific dataset with a teammate via the "Copy link" button, which also carries the pinned seed.
Are the address, city, state and country fields geographically consistent with each other?
No, deliberately not — each is drawn independently, so a generated row might pair a city with a state or country it doesn't actually belong to. Building a real place hierarchy (which cities belong to which state, which states to which country) is a much bigger dataset than this tool curates; if geographic consistency matters for your use case, treat these fields as independently plausible-looking placeholders, not a real gazetteer.
Why aren't the generated UUIDs from crypto.randomUUID()?
Because a pinned seed needs to reproduce every field identically, including UUIDs, and the browser's real UUID generator is intentionally not seedable — that's what makes it a good source of real randomness elsewhere on this site (the UUID Generator tool). Here, a UUID-shaped value is instead derived from the same seeded generator as every other field, which is what makes "reuse this seed" apply to the whole row consistently.
Is there a limit on how many rows I can generate?
Yes, 1,000 rows per batch — generation runs synchronously in your browser tab, and 1,000 rows across several fields is already a substantial fixture for UI testing, prototyping or a CSV import trial. A larger dataset is usually better produced by a real seeding script anyway, where "reproducible from a seed" matters even more.
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