Mock Data Generator

Define a schema of fields, pick a count, and get N records of realistic-looking data back. 32 field kinds: names, emails, addresses, dates, IDs, Luhn-valid credit cards, mod-97-valid IBANs, IPv4 / IPv6 / MAC, lorem ipsum, and more. Output as JSON, CSV, TSV, SQL INSERT, or NDJSON. Optional seed for reproducible fixtures. Runs in your browser, nothing is uploaded.

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Click a kind to insert it at the cursor:

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About field kinds and params
  • Identity — uuid, mac (sep ":" or "-"), ipv4, ipv6.
  • People — firstName, lastName, fullName, username, jobTitle.
  • Contact — email (params domain), phone (country: US/UK/DE/FR/IT/ES/JP), address, street, city, state, country, zip.
  • Time — date and datetime accept ISO from / to; time.
  • Numbers — int and float (params min, max, decimals); age (min / max).
  • Boolean — boolean (50/50).
  • Web — url, domain, tld, color (#rrggbb), lorem (param words).
  • Financial — iban (param country from a 46-country registry; check digits satisfy ISO 7064 mod-97), creditCard (Luhn-valid; brand any / visa / mastercard / amex; PAN is masked before output).
  • Choice — enum (param values: array of strings).

Reproducibility — set a seed (any text or number) and the same schema + seed always produces the same records. Leave the seed empty for fresh randomness on every click. Records are generated entirely in your browser; nothing is uploaded.

About this tool

Mock Data generates realistic-looking fake data for tests, demos, and development fixtures. The schema covers names, emails, phone numbers, street addresses, cities, countries, dates, UUIDs, credit card numbers (Luhn-valid but not real), company names, job titles, and IP addresses. Each row is generated independently with a stable seed option so you can reproduce the same dataset across runs. The output can be downloaded as JSON, CSV, or SQL INSERT statements. Nothing leaves your browser — the data is generated and held in memory only.

When to use it

  • Seeding a development database with realistic-looking users before the real schema is finalised
  • Producing a fixture file for a test suite that needs a known set of records
  • Filling a UI demo with plausible data so screenshots look like a real product
  • Generating sample CSV files for a documentation page or a tutorial

When not to use it

  • You need real data for a production system — fake data must never be used as a substitute for real data in production
  • You need to satisfy a regulatory requirement (PCI-DSS, GDPR) — the credit card numbers are Luhn-valid but the rest of the PAN structure is fictional. Use a PCI-compliant test card provider for compliance work
  • You need locale-specific data fidelity beyond the available set — the generator covers the most common fields but does not produce, for example, real postal codes that match the addresses it generates

Frequently asked questions

Are the credit card numbers real?

No. They pass the Luhn checksum but the bank identification numbers do not correspond to real issuing banks. They will be rejected by any payment processor. Use them only for testing UI flows and form validation.

Can I generate the same data twice?

Yes. Use the seed option to fix the random sequence — the same seed with the same schema will produce the same output, so tests can rely on the fixture.

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