What is a mock data generator used for?

A mock data generator creates realistic but fake records so you can populate a database, test an API, demo a UI, or load-test a system without using real customer data. It saves you from hand-typing test rows and keeps sensitive production data out of your test environment.

How many rows can I generate?

You can generate up to 50,000 rows in a single batch. Everything runs in your browser, so very large datasets depend on your device memory — for millions of rows, generate in batches and append the exported files.

What field types are supported?

The tool covers identity (auto-increment ID, UUID v4), people (first/last/full name, email, username, phone, job title), business and location (company, street, city, country), tech (IPv4, URL, hex colour), numbers (integer, decimal, price, boolean), time (date, ISO datetime), custom enums from your own list, and lorem paragraphs.

Can I get the same data again later?

Yes. Generation is fully deterministic from the seed you enter. Keep the same schema and seed and you will get byte-for-byte identical rows every time, which is ideal for reproducible tests and fixtures. Change the seed (or click Shuffle seed) for a fresh dataset.

How do the JSON, CSV and SQL exports differ?

JSON gives you a pretty-printed array, optionally wrapped in a root key such as records. CSV produces a header row plus comma-separated values with a UTF-8 BOM so spreadsheets open it cleanly. SQL produces one INSERT statement per row against the table name you choose, with strings quoted and escaped and nulls written as NULL.

Can fields be empty or NULL?

Yes. Each field has a Null % option. Set it above zero and that share of rows will be null (NULL in SQL, null in JSON, empty in CSV), which is useful for testing how your code handles missing values.

Completely. The generator runs 100% in your browser with no network calls, your schema is saved only in this browser local storage, and no data is ever uploaded to any server.

Mock Data Generator — Gera Tools

A mock data generator that lets you design a schema of typed fields and spin up thousands of realistic test records in seconds — then export them as JSON, CSV, or ready-to-run SQL INSERT statements. It is built for developers, QA engineers, and anyone who needs to populate a database, seed a staging environment, demo a UI with believable content, or stress-test an API without ever touching real customer data.

Instead of hand-typing fixture rows or copying production records (a privacy and compliance hazard), you describe the shape of the data once — a name column, an email column, an integer age between 18 and 80, a status drawn from your own list of values — and the tool fills in convincing values for every row. Because generation is driven by a repeatable seed, the same schema and seed always produce the exact same dataset, which makes your test fixtures reproducible and your CI runs stable.

How it works

You start by building a schema. Each field has a name (the column or JSON key) and a type chosen from a rich menu: identity types like auto-increment ID and UUID v4; people types such as full name, email, username, phone, and job title; location and business types like company, street address, city, and country; technical types including IPv4, URL, and hex colour; numeric types (integer and decimal ranges, price, boolean); time types (date and ISO datetime with year bounds); a custom enum where you supply a comma-separated list of allowed values; and lorem paragraphs for free text. Number, date, and ID types expose extra options — min and max bounds, decimal places, and a start value — and every field has a Null % control to inject missing values.

When you click Generate, the tool seeds a fast deterministic pseudo-random generator and fills each row. The same seed yields identical output, so you can share a seed with a teammate and you both get the same fixtures. The preview table shows the first ten rows; the export step then serialises the full dataset. Everything happens locally in your browser — there is no server, no upload, and no sign-up.

Example

Suppose you need to seed a users table for a demo. You add fields: id (auto-increment), first_name, last_name, email, age (integer 18–80), country, status (enum: active, pending, suspended), and created_at (datetime 2020–2025). Set the row count to 1,000, leave the seed as gera-001, and click Generate.

Switch the export format to SQL and you get statements like INSERT INTO users (id, first_name, last_name, email, age, country, status, created_at) VALUES (1, 'Olivia', 'Mensah', '[email protected]', 34, 'Kenya', 'active', '2023-07-12T09:41:05Z'); — one per row, strings escaped and nulls written as NULL. Switch to CSV for a spreadsheet-friendly file, or JSON for an array you can drop straight into an API mock. Re-run with the same seed any time to reproduce the identical set.

Every value is generated in your browser — no data is uploaded or stored on any server.