Mock JSON Generator
Build a realistic fake dataset for seeding a database, a demo UI or an API stub.
[
{
"id": 1,
"name": "Ken Perlman",
"email": "alan.perlman@soylent.com",
"city": "London",
"active": false
},
{
"id": 2,
"name": "Linus Ritchie",
"email": "alan.hamilton@umbrella.com",
"city": "Lisbon",
"active": false
},
{
"id": 3,
"name": "Grace Ritchie",
"email": "ken.liskov@umbrella.com",
"city": "Lisbon",
"active": true
},
{
"id": 4,
"name": "Margaret Liskov",
"email": "margaret.ritchie@initech.com",
"city": "Austin",
"active": false
},
{
"id": 5,
"name": "Radia Lovelace",
"email": "linus.liskov@hooli.com",
"city": "Austin",
"active": true
}
]- Seeding a local database with believable rows before building a UI
- Filling a table or list component to check pagination and overflow behaviour
- Creating a CSV fixture for an import feature you are testing
- Producing demo data for a screenshot or a client walkthrough
Free and private. Everything runs on your own device, with no account and no uploads.
Building a list view, seeding a database or demoing a dashboard all need the same thing: a few dozen rows of data that look real. This generator lets you define the exact fields you need (names, emails, cities, prices, dates, UUIDs, booleans and more) and produces that dataset as JSON, CSV or ready-to-run SQL INSERT statements. The generator is seeded, so Regenerate gives you a fresh set while the same seed always rebuilds the same rows, and everything is produced locally without an API key or a rate limit.
Sequential ids, UUIDs, first and last names, full names, emails, usernames, cities, companies, phone numbers, booleans, integers, prices, dates, timestamps, sentences, paragraphs, URLs, IPv4 addresses and hex colours.
It is generated from a seeded pseudo-random source, so the output is stable until you press Regenerate. That makes it safe to use in a test fixture where a changing dataset would break assertions.
Yes. It emits one INSERT per row against a mock_data table with your field names as columns: rename the table to match your schema before running it.
No. Names are drawn from a small list of computing pioneers and emails are built from fictional company domains, so nothing maps to a real person or a deliverable mailbox.
Up to five hundred per batch, which keeps the preview responsive. For larger datasets, generate several batches and concatenate them.
- 1
Define your fields
Name each field and pick the kind of value it should hold.
- 2
Set the row count
Choose anywhere from one row up to five hundred.
- 3
Pick an output format
Switch between JSON, CSV and SQL INSERT statements.
- 4
Copy or regenerate
Copy the dataset, or hit Regenerate for a completely different batch.
- ·Name your fields to match your real schema so the SQL and JSON drop straight into your project.
- ·Use the id field type for the primary key: it counts up from one rather than producing random numbers.
- ·Generate one row first to check the shape, then raise the count once the fields are right.