Mock Data Generator

Build robust fake data schemas and generate thousands of rows of realistic JSON/CSV data instantly.

Number of Rows:

Data Schema

Field Name

Data Type

Action

About Mock Data Generator

Mock Data Generator creates realistic fake datasets for testing, development, and demonstrations. Generate names, email addresses, phone numbers, addresses, dates, UUIDs, credit card numbers, company names, IP addresses, and more — in JSON, CSV, or SQL format. Configurable row count and field schema let you generate exactly the test data your application needs.

How it Works

1Define your schema: select field types and add columns.
2Set the number of rows to generate.
3Choose output format: JSON array, CSV, or SQL INSERTs.
4Generate and download the mock dataset.

Frequently Asked Questions

Is the generated data truly random?

The data uses the browser's Math.random() seeded with locale-specific wordlists for realistic-looking names, addresses, and company names. It is statistically random but not cryptographically secure. Do not use generated data for security purposes (e.g. as tokens or keys) — use crypto.getRandomValues() for that.

Can I generate relational data with consistent foreign keys?

The generator creates flat records. For relational data with consistent foreign key relationships, generate the parent table first with UUID primary keys, then generate the child table referencing those specific IDs. A script-based approach (Faker.js or Factory Boy) is better for complex relational fixtures.

Are the generated credit card numbers valid?

Generated credit card numbers pass the Luhn algorithm check (used for format validation) but are not real card numbers and will be declined by any payment processor. They are safe to use in forms, test environments, and demos that validate card format without charging.

Can I use this data in automated tests?

Yes. Export in JSON or CSV format and import into your test fixtures. For tests that require deterministic, repeatable data, consider seeding your test runner with a fixed random seed or hardcoding specific values rather than regenerating mock data on each test run.

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