❯ Mock Data Generator

A mock data generator produces realistic-looking fake user, order, log, or geographic records for testing. This tool takes a JSON template with {{token}} placeholders, runs it N times through faker, and assembles the records as JSON / JSON Lines / CSV / SQL INSERT — entirely in your browser.

How to use the mock data generator

1. Pick a sample template (User, Order, Server log, Geo coordinate) or write your own JSON. Use {{token}} placeholders where you want randomness.
2. Set count and output format. 1–5000 records. JSON for general use, NDJSON for streaming pipelines, CSV for spreadsheets and SQL bulk-load tools, INSERT for direct paste into psql/mysql/sqlite.
3. Lock the seed (optional). Click Lock seed once and every subsequent generation with the same template uses that seed. Reproducible fixtures.
4. Copy or download. Output is ready to paste, save, or import.

Template syntax

A template is a JSON object. Tokens look like {{name}} or {{name(arg1, arg2)}}. Examples:

{
  "id": "{{uuid}}",
  "name": "{{fullName}}",
  "email": "{{email}}",
  "active": {{boolean}},
  "role": "{{pick(admin,editor,viewer)}}",
  "age": {{number(18,80)}},
  "score": {{float(0,100,2)}},
  "createdAt": "{{isoDate}}"
}

After token expansion, each record must parse as valid JSON. Wrap string tokens in "…"; leave number/boolean tokens unquoted.

Token cheat sheet

Full reference (with example values) is in the tool’s Available tokens dropdown.

Output format comparison

JSON array

[
  { "id": "550e…", "name": "Ada Lovelace" },
  { "id": "1a2b…", "name": "Grace Hopper" }
]

Default. Easy to paste into a fixture file, send to JSON.parse, or upload as a single payload.

JSON Lines (NDJSON)

{"id":"550e…","name":"Ada Lovelace"}
{"id":"1a2b…","name":"Grace Hopper"}

One record per line. Streaming-friendly. BigQuery, Snowflake, DuckDB, jq, and many ETL pipelines prefer this over a single huge array.

CSV

id,name
550e…,Ada Lovelace
1a2b…,Grace Hopper

Header row included. Values with commas or quotes get RFC 4180-quoted automatically. Open in Excel, Google Sheets, or COPY straight into Postgres.

SQL INSERT

INSERT INTO users (id, name) VALUES
  ('550e…', 'Ada Lovelace'),
  ('1a2b…', 'Grace Hopper');

Single bulk insert. Identifiers that are not valid SQL get double-quoted. Booleans become TRUE/FALSE. Paste straight into a database client.

Determinism with seeds

Faker’s PRNG can be seeded. Click Lock seed and the tool generates a random 6-digit seed, locks it, and re-generates. Every subsequent generation with the same template + same seed produces byte-identical output. Useful for:

• Reproducible test fixtures (commit the seeded output to git, regenerate to verify)
• Bug repros (your colleague pastes the share link and sees the same broken data)
• Snapshot testing (use the same seed across runs)

Click Clear to drop back to fresh randomness on every generation.

When NOT to use a mock data generator

• Production data. Generated values are realistic-looking but not statistically representative of real users. Don’t use for ML training or analytics QA.
• Security testing. The values are deterministic per seed and follow faker’s distributions. Real users have edge cases (Unicode names, very long emails, mixed alphabets) faker doesn’t fully cover.
• Compliance datasets. GDPR / HIPAA test data should use officially-synthesized data from your data team, not a generic generator.

How it compares

Privacy and security

A static HTML page with a small initial JavaScript bundle. The @faker-js/faker library lazy-loads on first generation (~1 MB raw, ~280 KB gzip). Once loaded it runs entirely in your browser tab. Templates and output never leave the page.

Related tools

• JSON to Types Generator — once you’ve generated mock data, derive types from the shape ({{uuid}} becomes string, {{number}} becomes number).
• JSON Formatter & Error Reviewer — pretty-print the generated output or debug a template that’s not producing valid JSON.
• JSON to CSV Converter — alternate CSV path with more control over nested-object flattening.

Frequently asked questions