DT Dev Tools

Mock Data Generator

Generate fake test data as JSON, NDJSON, CSV or SQL INSERT: names, emails, addresses, UUIDs, IBANs, IPs, prices and dates, with a seed for repeatable output.

πŸ”’ Runs entirely in your browser β€” nothing is uploaded

Fields
Identity
Person
Work
Address
Internet
Finance
Time
Other

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What Mock Data Generator does

Mock Data Generator creates fake datasets for prototypes, UI states, demos, tests, documentation, and import experiments. Pick from 29 fields grouped by topic β€” identity (id, uuid), person (first and last name, username, email, phone, age), work (company, job title), address (street, city, country, postal code, latitude, longitude), internet (IPv4, IPv6, MAC address, URL, hex colour), finance (IBAN, price, quantity), time (date, ISO datetime) and other (boolean, sentence). Values in one row belong together, so the email and username are derived from the same person's name and the postal code matches the city's format.

Export the rows as pretty JSON for API mocks, NDJSON for streaming and bulk-import tools, CSV for spreadsheets, or ready-to-run SQL INSERT statements with properly escaped strings for seeding a local database. Add a seed and the output becomes deterministic: the same seed, fields and row count give exactly the same data on every machine, which keeps snapshot tests, screenshots and bug reports reproducible.

Private browser-based workflow

All processing happens inside your browser with client-side JavaScript. The tool does not upload snippets, configuration values, generated data, query text, or copied examples to a server, and it does not require an account. That local-only model matters for developer utilities because the content pasted into them often looks like real application data. Keeping the work local reduces risk and keeps the interaction fast because there is no network round trip.

Practical tips

Mock data should never be mistaken for production-quality anonymization. If you need privacy-safe analytics, compliance-grade de-identification, or statistically realistic synthetic data, use a dedicated process. Generated IBANs have valid check digits but do not belong to real accounts, emails use reserved example domains, and IPv6 addresses come from the 2001:db8::/32 documentation range, so the data is safe to show in demos. Keep row counts modest so the browser remains responsive, and review field names and formats before feeding the data into strict schemas.

How to use

  1. Select fieldsChoose from 29 fields such as uuid, name, email, address, IBAN, IP, price and dates.
  2. Set count, format and seedEnter up to 1000 rows, pick JSON, NDJSON, CSV or SQL, and optionally a seed.
  3. Copy or downloadReview the output, then copy it or download a file.

Frequently asked questions

Does this use an external fake-data service?
No. It uses embedded word lists and random generation directly in the browser.
Which formats can I export?
JSON, NDJSON (JSON Lines), CSV, and SQL INSERT statements with a table name you choose β€” copy them or download a file.
Can I get the same data again?
Yes. Enter a seed such as demo-42 and the same seed, fields and row count always produce identical rows, which is useful for snapshot tests and shared demos.
Is the data realistic?
It is realistic enough for prototypes and fixtures β€” IBANs even pass checksum validation and IPv6 addresses use the documentation range β€” but every value is fake.