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Mock Data Generator

Build a schema and generate fake records as JSON, CSV, SQL inserts, or NDJSON. Add a seed to get the same data every time.

Fields

Everything runs in your browser. Nothing is uploaded.

Output format

The same seed and schema always produce the same dataset. Up to 1,000 rows.

Data

How to generate mock data online

  1. Define your fields

    Name each column and pick a type such as full name, email, UUID, integer, date, city, or a custom pick list.

  2. Choose an output format

    Select JSON, CSV, SQL inserts, or NDJSON, and the dataset re-renders instantly in that format.

  3. Set rows and an optional seed

    Pick how many rows you need, up to 1,000, and enter a seed if you want the exact same data on every run.

  4. Copy or download the result

    Copy the dataset to your clipboard or download it as a file with the right extension for your format.

Why use this tool

Ten field types

Full names, emails, UUIDs, integers, floats, booleans, dates, cities, lorem text, and custom pick lists cover most fixtures.

Four export formats

The same dataset serializes as a JSON array, CSV with a header row, a multi-row SQL INSERT, or NDJSON, each escaped correctly.

Reproducible with a seed

Enter any seed text and the same schema produces the identical dataset every time, on any machine.

Consistent rows, tunable nulls

Each row gets one person, so its email matches its name. Every field takes a null chance from 0 to 100 percent.

Runs entirely in your browser

Data is generated on your device. Nothing is uploaded and no account is needed.

About this tool

The mock data generator builds realistic fake records for testing, demos, seeding databases, and designing with believable content. You define a schema of named fields, each with a type: full name, email, UUID, integer, float, boolean, date, city, lorem text, or a pick list of your own comma separated options. The tool generates rows the moment anything changes, so there is no run button to hunt for.

Rows are internally consistent. Each row is built around one generated person, so a name field and an email field in the same row agree with each other instead of pairing a random name with an unrelated address. Every field also has a null chance, which is useful when you need to test how your code handles missing values. Dates land between 2020 and 2025 in ISO format, integers range up to 9,999, and floats carry two decimals.

Output comes in four formats: a pretty printed JSON array, CSV with a proper header row and quoted values, a multi-row SQL INSERT with a table name you control, or NDJSON with one object per line. Quoting and escaping follow each format, so commas in text or apostrophes in names never break the file. You can generate up to 1,000 rows at a time, and an optional seed makes the output reproducible so a teammate with the same schema and seed gets byte identical data. For single identifiers rather than whole datasets, try the UUID v5 generator or the ObjectId generator.

Frequently asked questions

How does the mock data generator work?
You define fields with a name and a type, and the tool generates rows instantly as you edit the schema, row count, seed, or format. The result is serialized as JSON, CSV, SQL inserts, or NDJSON, ready to copy or download.
Is my schema or data uploaded anywhere?
No. Everything runs in your browser; nothing is sent to a server. The generated records never leave your device.
What does the seed do?
A seed makes the output deterministic. The same schema, row count, and seed always produce the identical dataset, which is useful for repeatable tests and shared fixtures. Leave it blank to get fresh data on every regenerate.
Which field types are supported?
Full name, email, UUID, integer, float, boolean, date, city, lorem text, and a pick list where you supply comma separated options. Emails are derived from the same generated name used in that row.
How many rows can I generate?
Up to 1,000 rows per run with up to 20 fields. The cap keeps generation instant while covering typical fixtures, seeds, and demo datasets. For bigger sets, generate in batches with different seeds.
Is the generated data safe to use?
Yes. Names and cities come from small built-in word lists, emails use reserved example domains, and identifiers are random, so no real person or account is referenced.

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