How is this different from faker.js or Mockaroo?

faker.js is an npm library — you write code to call it, and it ships megabytes of locale data to your bundle. Mockaroo is a hosted service that sends your schema to a server and rate-limits free users to 1,000 rows. This tool is a single page that runs in your browser: no install, no signup, no quota, no upload. Embedded word lists are ~25 KB total. Trade-off — we cover 18 common field types and three output formats, not the 150+ Mockaroo-style domain-specific generators. For ad-hoc seeding of a database, populating a Storybook fixture, or stress-testing a CSV importer, that's plenty.

Can I get reproducible output (same seed)?

Yes. Set a numeric seed in the schema controls and the generator becomes deterministic — same seed + same schema + same row count always produces byte-identical output. Internally we use a mulberry32 PRNG seeded from your value. Leave the seed blank and generation falls back to Math.random for fresh data on every run. Reproducible seeds matter when you commit a fixture to git or want your tests to fail the same way every CI run.

Is this data safe to use in production tests?

Yes for QA and integration testing — none of the names, emails, or addresses correspond to real people; they're random combinations from public name and place lists. Do not use these emails to send real mail (the domains may resolve to real servers), and do not present this data to end users as if it were real. Treat it like Lorem Ipsum: structurally realistic, semantically fictional.

What's a realistic row count for performance testing?

We cap the slider at 1,000 rows per generation because anything bigger starts to feel slow in a single browser tick. For real load testing of a database or API, generate 1,000 rows with a fixed seed, download the SQL output, then loop it server-side with INSERT… SELECT or a generate_series multiplier to scale to millions without re-running the browser tool. The same seed will produce the same shape every time, so you can reproduce a failed test run with a one-liner.

How is the SQL output structured?

We emit one `INSERT INTO table_name (col1, col2, …) VALUES (…), (…), …;` statement with all rows batched together — the standard form accepted by PostgreSQL, MySQL, SQLite, and SQL Server. Strings are single-quoted with embedded single quotes escaped by doubling (`O'Brien` → `'O''Brien'`). Booleans become `TRUE` / `FALSE`. Dates are ISO 8601 strings. You can rename the target table in the schema controls before downloading.

Seed a Postgres dev database before a demo

You need 300 plausible user rows for a sales demo tomorrow. Add id, fullName, email, country, and a created_at date field, set the table name to users, generate 300 rows with seed 42, and download the SQL. Paste it straight into psql. Next week, rerun with seed 42 and you get the exact same 300 rows, so screenshots stay consistent.

Stress-test a CSV importer against messy names

Your upload parser broke once on a name like O'Brien. Generate 1000 rows with lastName, email, and a float balance column, export CSV, and feed it to your importer. The name list includes apostrophes and accents, so you exercise quoting and escaping paths that a hand-typed fixture of five rows would never hit.

Populate Storybook fixtures for a data table

Your component needs 25 rows to show pagination, sorting, and truncation of long emails. Define id, fullName, email, enum role (admin, editor, viewer), and a boolean active field, generate 25 rows as JSON with a fixed seed, and drop the array into a .fixtures.ts file. The deterministic seed means visual regression snapshots never flicker.

Build a reproducible failing test case

A bug only shows up with certain row shapes. Generate 50 rows with seed 7, save the JSON to your repo, and write a test that loads it. When CI fails, every engineer reproduces the identical input by regenerating with seed 7, no need to commit a 5000-line dump or guess which random run triggered the edge case.

Mock Data Generator — JSON, CSV, SQL

Generate realistic mock JSON / CSV / SQL data — names, emails, dates, addresses, nested objects.

Runs locally
Category Generator
Best for Starting from a blank page without committing to the first result.

Schema

Rows: 10

Format

SQL table name

Seed (optional)

Output

[
  {
    "id": 1,
    "name": "Wayne Patel",
    "email": "madison.king@mail.example",
    "city": "Raleigh",
    "signup_date": "2024-08-18",
    "active": false
  },
  {
    "id": 2,
    "name": "Willie Moreno",
    "email": "barbaravasquez@inbox.dev",
    "city": "Berlin",
    "signup_date": "2026-03-06",
    "active": false
  },
  {
    "id": 3,
    "name": "Justin Bell",
    "email": "cynthia.west@sample.org",
    "city": "New York",
    "signup_date": "2024-12-19",
    "active": false
  },
  {
    "id": 4,
    "name": "Alan Weaver",
    "email": "michaelpierce@mock.app",
    "city": "Brussels",
    "signup_date": "2022-11-16",
    "active": false
  },
  {
    "id": 5,
    "name": "Terry Hudson",
    "email": "michael.jordan@fake.net",
    "city": "Madrid",
    "signup_date": "2025-01-15",
    "active": true
  },
  {
    "id": 6,
    "name": "Rachel Spencer",
    "email": "cynthiaturner@sample.org",
    "city": "Portland",
    "signup_date": "2023-06-20",
    "active": true
  },
  {
    "id": 7,
    "name": "Russell Campbell",
    "email": "tyler.myers@inbox.dev",
    "city": "Casablanca",
    "signup_date": "2024-06-02",
    "active": true
  },
  {
    "id": 8,
    "name": "David Elliott",
    "email": "dennismason@sample.org",
    "city": "Singapore",
    "signup_date": "2023-05-28",
    "active": true
  },
  {
    "id": 9,
    "name": "Billy Stone",
    "email": "judith.gutierrez@corp.example",
    "city": "Houston",
    "signup_date": "2025-12-21",
    "active": true
  },
  {
    "id": 10,
    "name": "Maria Rice",
    "email": "jasongray@example.com",
    "city": "Jakarta",
    "signup_date": "2023-10-24",
    "active": false
  }
]

What this tool does

Free online mock data generator for developers, QA engineers, and designers who need realistic test data right now. Define a schema by adding fields (name + type), pick from 18 generator types — id, uuid, firstName, lastName, fullName, email, username, phone, address, city, country, date, boolean, int, float, enum (custom values), paragraph, url — choose how many rows (1–1000), then export as JSON, CSV, or a ready-to-run SQL INSERT batch. Reproducible with a seed: the same seed + schema always produces the same data, perfect for stable snapshots in tests. 100% client-side: schema, generation, and download never leave your browser. Embedded word lists (200+ first names, 200+ last names, 100+ cities, 50+ countries) keep output realistic without a single network call.

Tool details

Input: Text + Numbers; The page exposes text boxes, numeric controls, file pickers, or structured inputs depending on the tool.
Output: Live result + Copy + Download; The result area focuses on usable output, with copy, download, or preview actions when supported.
Privacy: Browser-side processing; The main tool logic does not call an external API, so inputs normally stay in the current tab.
Save / share: No account required; Open the page and use it; whether results survive refresh depends on the tool.
Performance budget: Initial JS <= 35 KB; No WASM budget is declared, keeping the tool quick to open on mobile.
Best fit: Generator · Developer; Category and role tags drive related tools, internal links, and quick fit checks.

How to use

1. Input

Paste or drop your content into the tool panel.
2. Process

Click the button. All processing is local in your browser.
3. Copy / Download

Copy the result or download to disk in one click.

How Mock Data Generator fits into your work

Use it to get a strong first draft, starter asset, or structured output that you can edit before publishing.

Generation jobs

Starting from a blank page without committing to the first result.
Creating repeatable drafts, names, templates, or placeholder assets.
Exploring options before choosing the one that fits the job.

Generation checks

Review generated output before it reaches a customer, page, or document.
Change defaults when you need a specific brand voice, format, or audience.
Keep only the parts that match the real task.

Good next steps

These links move the current task into a more complete workflow.

Real-world use cases

Seed a Postgres dev database before a demo
You need 300 plausible user rows for a sales demo tomorrow. Add id, fullName, email, country, and a created_at date field, set the table name to users, generate 300 rows with seed 42, and download the SQL. Paste it straight into psql. Next week, rerun with seed 42 and you get the exact same 300 rows, so screenshots stay consistent.
Stress-test a CSV importer against messy names
Your upload parser broke once on a name like O'Brien. Generate 1000 rows with lastName, email, and a float balance column, export CSV, and feed it to your importer. The name list includes apostrophes and accents, so you exercise quoting and escaping paths that a hand-typed fixture of five rows would never hit.
Populate Storybook fixtures for a data table
Your <UserTable> component needs 25 rows to show pagination, sorting, and truncation of long emails. Define id, fullName, email, enum role (admin, editor, viewer), and a boolean active field, generate 25 rows as JSON with a fixed seed, and drop the array into a .fixtures.ts file. The deterministic seed means visual regression snapshots never flicker.
Build a reproducible failing test case
A bug only shows up with certain row shapes. Generate 50 rows with seed 7, save the JSON to your repo, and write a test that loads it. When CI fails, every engineer reproduces the identical input by regenerating with seed 7, no need to commit a 5000-line dump or guess which random run triggered the edge case.

Common pitfalls

Forgetting to set a seed, then wondering why your committed fixture differs from CI output. Leave seed blank only for throwaway data; set seed 42 for anything you save.
Using generated emails to send real mail. The domains may resolve to live servers, so a test blast can hit real inboxes. Route these through a sink like MailHog instead.
Capping at 1000 rows and assuming that's your load test. For real scale, generate 1000 with a fixed seed, then multiply server-side with INSERT…SELECT rather than re-running the browser.

Privacy

Everything runs in your browser. Your schema, the chosen seed, the row count, and every generated row stay in the page and the download file never touch a server. Nothing is written to the URL either, so a shared link carries only the tool address, not your field definitions or data. The embedded name and place lists ship with the page, so generation works fully offline once loaded.

FAQ

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Mock Data Generator — JSON, CSV, SQL

What this tool does

Tool details

How to use

1. Input

2. Process

3. Copy / Download

How Mock Data Generator fits into your work

Generation jobs

Generation checks

Good next steps

Real-world use cases

Seed a Postgres dev database before a demo

Stress-test a CSV importer against messy names

Populate Storybook fixtures for a data table

Build a reproducible failing test case

Common pitfalls

Privacy

FAQ

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