Mock Data Generator
Build your own schema and generate reproducible fake data as JSON, JSONL, CSV or SQL.
Templates
Schema
From 1 to 1000
Numeric or text — the same seed always generates the same dataset
Export format
[
{
"name": "Charles Rodriguez",
"email": "betty.wilson18@outlook.com",
"phone": "(312) 624-8654",
"document": "428.731.566-94",
"address": {
"street": "Sunset Blvd",
"number": 513,
"city": "New York",
"state": "NY",
"zipCode": "10083"
},
"age": 55,
"company": "Nexus Solutions LLC"
},
{
"name": "Jennifer Thomas",
"email": "jessica.smith18@hotmail.com",
"phone": "(512) 809-3194",
"document": "400.552.623-33",
"address": {
"street": "Broadway",
"number": 2042,
"city": "San Diego",
"state": "CA",
"zipCode": "92150"
},
"age": 35,
"company": "Prism Technologies Corp."
},
{
"name": "Daniel Wilson",
"email": "margaret.johnson94@outlook.com",
"phone": "(404) 137-1109",
"document": "034.680.792-14",
"address": {
"street": "Maple St",
"number": 8219,
"city": "San Diego",
"state": "CA",
"zipCode": "92152"
},
"age": 25,
"company": "Summit Holdings Co."
},
{
"name": "Susan Lopez",
"email": "david.lopez32@icloud.com",
"phone": "(312) 449-8431",
"document": "698.452.163-74",
"address": {
"street": "Franklin Ave",
"number": 4754,
"city": "San Diego",
"state": "CA",
"zipCode": "92109"
},
"age": 69,
"company": "Vertex Logistics LLC"
},
{
"name": "Nancy Wilson",
"email": "michael.rodriguez17@icloud.com",
"phone": "(415) 206-6289",
"document": "837.318.307-87",
"address": {
"street": "Franklin Ave",
"number": 2090,
"city": "San Antonio",
"state": "TX",
"zipCode": "78290"
},
"age": 56,
"company": "Cobalt Systems LLC"
},
{
"name": "Nancy Anderson",
"email": "william.garcia34@gmail.com",
"phone": "(312) 993-5723",
"document": "911.523.446-09",
"address": {
"street": "Washington Blvd",
"number": 3273,
"city": "Houston",
"state": "TX",
"zipCode": "77089"
},
"age": 49,
"company": "Orbit Holdings Corp."
},
{
"name": "Linda Miller",
"email": "william.miller67@yahoo.com",
"phone": "(213) 694-3142",
"document": "130.524.379-09",
"address": {
"street": "Park Ave",
"number": 8676,
"city": "Dallas",
"state": "TX",
"zipCode": "75215"
},
"age": 51,
"company": "Vector Logistics Inc."
},
{
"name": "John Gonzalez",
"email": "anthony.jones39@outlook.com",
"phone": "(404) 491-8594",
"document": "870.015.679-57",
"address": {
"street": "Highland Ave",
"number": 8529,
"city": "Dallas",
"state": "TX",
"zipCode": "75283"
},
"age": 55,
"company": "Zenith Services Co."
},
{
"name": "Daniel Miller",
"email": "donald.gonzalez33@hotmail.com",
"phone": "(213) 365-0199",
"document": "067.984.712-00",
"address": {
"street": "Main St",
"number": 4410,
"city": "Los Angeles",
"state": "CA",
"zipCode": "90029"
},
"age": 53,
"company": "Meridian Consulting Co."
},
{
"name": "Jennifer Wilson",
"email": "jessica.rodriguez47@outlook.com",
"phone": "(512) 331-3075",
"document": "152.556.410-22",
"address": {
"street": "Jefferson St",
"number": 8706,
"city": "Los Angeles",
"state": "CA",
"zipCode": "90006"
},
"age": 45,
"company": "Apex Technologies Co."
}
]What the Mock Data Generator does
Generates fake data to seed a test database, demo, or automated test fixture — without relying on a fixed set of fields like "Brazilian person". You build your own schema: choose which fields to include, in what order, and how each one should be generated.
Customizable schema, not a fixed one
Each row of the schema is a field with a name, a type, and type-specific options (e.g. a numeric field's range, a date field's period, a list field's values). The ready-made templates ("Brazilian Person", "Product") are just a starting point — fully editable once loaded.
Reproducible seed
The same seed, with the same schema and the same record count, always generates the exact same dataset — useful for automated test fixtures (the same data every time tests run) or for reproducing a bug reported with specific data. The seed accepts either a number or any text.
Always coherent addresses
The "Full address" field generates street, number, city, state, and ZIP/CEP that are always coherent with each other (never a city combined with another state's ZIP) — from a dataset curated for internal use by this tool, not a live lookup against an official registry (that's the CEP Lookup's job).
Frequently asked questions
Yes — the check digits are calculated with the same algorithm already used by the CPF/CNPJ Generator/Validator, so every generated document would pass validation in a real system. It's never a real document belonging to anyone — only mathematically valid.
No — all generation happens in your browser. The only time data leaves your machine is if you use the "Publish to Mock API" button, which sends the dataset to create a test endpoint (stored on the server until it expires, like any other mock).
It's the starting point of a deterministic pseudo-random number generator — the same seed always walks the same sequence of values, so the same schema + the same seed + the same record count always reproduce an identical dataset. Changing any of the three (schema, seed, or count) changes the result.
No — they're fictitious, drawn from a bank of common names/cities/formats in Portuguese or English (matching the interface language at generation time). Addresses use real Brazilian/American cities only so the city-state-ZIP coherence looks plausible — never a specific address belonging to anyone.
Yes — the "Publish to Mock API" button takes the generated JSON straight to the Mock API's form, already filled in, ready to review status code/expiration and create the endpoint. Very large datasets (over 64 KB as JSON) don't fit the Mock API's limit — reduce the record count or download the file instead.
If two schema rows use the same field name, the second one gets an automatic suffix (e.g. "email" and "email_2") — this prevents a column from silently disappearing in the generated JSON (where repeated keys would normally overwrite each other).
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