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A free mock & fake data generator that runs in your browser

This mock data generator builds realistic, fictional records for whatever schema you need and exports them as JSON, CSV, SQL or XML. There's nothing to install and no account to create — you define your fields, set a row count, and generate as many rows as you like, entirely on your device. It's the practical alternative to hand-writing fixtures or asking ChatGPT for sample rows one prompt at a time.

Seeding a database, populating a UI mockup, load-testing an import, or building a demo — a solid fake data generator saves hours and keeps real customer data well away from your test environments.

Build any schema, field by field

Add fields, name each column, and choose from over two dozen field types: auto-increment IDs, UUIDs, first / last / full names, usernames, emails, phone numbers, streets, cities, states, countries, postal codes, companies, job titles, product names, dates and date-times, booleans, integers and decimals with min/max ranges, prices, color hex, IP addresses, URLs, lorem text, and your own custom list. Reorder or remove fields at any time, and give any field a blank/null probability so your test data includes the missing values real systems have to handle.

In a hurry? One-click presets drop in ready-made schemas for users, products, orders, employees and blog posts — then tweak them to fit.

Export to JSON, CSV, SQL or XML

Switch formats without regenerating. Export an array of objects as JSON, a spreadsheet-ready CSV with proper quoting, batch SQL INSERT statements for your seed scripts, or XML — plus copy-paste-ready JavaScript and TypeScript arrays (the TypeScript option even infers an interface). Numbers stay unquoted, booleans become TRUE/FALSE in SQL, and empty fields become NULL, so the output drops straight into your stack.

Copy the result to your clipboard, or download it as a .json, .csv, .sql or .xml file ready to import.

Locale-aware, realistic and reproducible

Pick a locale — US, UK, India, Germany or France — and names, streets, phone formats and postcodes shift to match the region, while emails derive from the generated name so every row is internally consistent. Need the same dataset twice? Enter a seed and the output becomes fully reproducible: the same seed always rebuilds the identical rows, which is perfect for snapshot tests and shareable bug reports.

Unlimited rows, 100% private — how it compares to Mockaroo

Server-based tools like Mockaroo cap free rows at 1,000 per download, require an account for saved schemas, and generate your data on their servers. This random data generator works differently: free, no account needed, and the Faker-style generation runs entirely in your browser so your schema and data never leave your device. Generate hundreds, thousands or tens of thousands of rows of synthetic test data — the only limit is your own machine.

Common uses for fake data

Developers reach for a dummy data generator to seed development and staging databases, fill out admin tables and dashboards for screenshots, generate CSV and SQL fixtures for automated tests, prototype API responses, and benchmark pagination, search and imports at scale. Designers use it to fill mockups with believable content instead of lorem ipsum, and QA teams use seeded datasets to reproduce edge cases. Whenever you need believable data fast — without touching real user records — this is the right tool for it.

GDPR, privacy by design, and why real customer data must never reach dev environments

GDPR Article 25 codifies 'data protection by design and by default', which in practice means personal data must be kept out of systems that don't need it — including development laptops, staging servers, CI runners, and log files. Using a production database dump in staging, even with good intentions, exposes real names, email addresses and phone numbers to every developer with database access, to third-party monitoring tools, and to any accidental log line that captures a query result. A single misconfigured staging environment has caused multi-million-euro fines under GDPR.

The safe alternative is to build development and staging data pipelines on synthetic test data from the start. Generated fake records are structurally identical to production data — same column types, same cardinality, same referential constraints — so every query, migration and import script that works in staging will work in production, without any real personal information ever leaving the production boundary. Regulators in the EU, UK and California (CCPA) increasingly treat the use of real PII in non-production environments as a compliance gap, making synthetic data not just a convenience but a legal safeguard.

Beyond regulatory risk, fake data removes a chilling effect on development. When every developer and QA engineer can generate a fresh, realistic dataset on demand, there is no bottleneck waiting for a sanitised production dump, no arguments about data-sharing agreements, and no risk of accidentally emailing a test order confirmation to a real customer address sitting in the staging database.

Why realistic fake data matters more than random noise

The earliest test data was random noise — random strings in name fields, random integers in phone fields. That kind of data passes most business-logic tests but misses a whole class of real-world bugs. A first-name field filled with 'xxxxxxxx' will never expose the multi-byte encoding bug that crashes when a user named Müller or 李明 signs up. A phone field containing '12345' will never trigger the off-by-one in a formatter that assumes ten digits. Realistic, locale-aware fake data — names drawn from actual frequency tables, emails derived from those names, phone numbers matching real number-plan formats — is far more likely to exercise the edge cases that only show up once a product reaches real users.

This is the core philosophy behind libraries like Faker.js: generate data that is structurally indistinguishable from production data, so that a test suite run on fake data is a genuine proxy for production behaviour. The same principle applies to UI testing: a dashboard built with 'Lorem Name' in every cell will look fine in a screenshot, but a dashboard seeded with 'Bartholomew Christodoulopoulos-Nakamura' will immediately reveal that your ellipsis truncation is off by 40 characters.

Blank and null values are equally important. Real databases are messy — optional fields are often empty, imported CSVs have gaps, users skip form fields. A null probability per field simulates that messiness in generated data, so your application's null-handling code is exercised before it encounters a real user who left their middle name blank.

Seed-based reproducibility and its role in testing

A pseudo-random number generator (PRNG) produces a deterministic sequence given the same initial value — the seed. Every good fake data library exposes this seed so that a test can record the exact seed used to generate its fixtures and replay them identically on any machine at any time. Without a seed, a flaky test that only fails when a generated email happens to contain an apostrophe is nearly impossible to reproduce; with a seed, you can pin the generation, open the same data in your debugger, and find the bug.

Seeded generation also enables snapshot testing: generate 500 rows with seed 42, render them to HTML, and save that HTML as the expected snapshot. On the next CI run, regenerate with the same seed, re-render, and diff. If the snapshot changes you know exactly which code path changed the output, not just that something changed. Tools like Jest's toMatchSnapshot and Playwright's visual comparison rely on this pattern.

In practice, teams often store seeds alongside their fixture files — a comment in the seed script, a seed key in a JSON fixture, or an environment variable in the CI configuration. When a bug is reported, attaching the seed that produced the offending dataset is the fastest way to hand off a reproducible case to another developer.

Fake data libraries, database seeding patterns, and CI/CD pipelines

Several open-source libraries implement the Faker pattern at scale. Faker.js (JavaScript/TypeScript) is the most widely used on the front end and in Node.js, with built-in support for over 70 locales and dozens of data categories including internet, commerce, finance and vehicle data. Faker (Python) mirrors the API for backend and data-engineering workflows. Bogus (.NET) follows the same paradigm and adds a Fluent API for defining entity factories. Datafaker (Java) replaces the now-archived Java Faker library and integrates with JUnit 5. All of them support seeded generation; all of them are open source and free. When your project is already running one of these, building on it directly is usually the right call. When you need a quick one-off dataset without adding a dependency — for a demo, a spreadsheet, or a bug report — a browser-based generator like this one is faster.

At the database level, fake data powers two distinct patterns: fixtures and factories. Fixtures are static files — a JSON or SQL file committed to the repository that always loads the same rows. They are fast and predictable but go stale as the schema evolves. Factories are code that generates rows on demand each test run, so they stay in sync with schema migrations automatically. Most modern test stacks (Rails FactoryBot, Laravel Model Factories, Django mixer, Prisma fixtures) use the factory pattern for unit and integration tests, and fall back to fixture files for slow integration tests that need a stable, large dataset loaded once per test session.

In a well-designed CI/CD pipeline, fake data appears at several stages. The unit-test stage runs factories in memory, no database required. The integration-test stage seeds a fresh database container with generated rows before the test suite starts and tears it down afterward. The staging environment is seeded once from a large generated dataset that mimics production volume, allowing load tests and performance profiling without any real user data. The result is a pipeline where every stage has the data it needs, generated on demand, at the right scale, with zero privacy risk.