Test an interface
Populate tables, forms, lists and demo screens with credible data in seconds.
Data Faker
Create up to 10,000 localized fake records with customizable fields, reproducible seeds and JSON, CSV or SQL exports. Everything stays in your browser.
- Clipboard
- Batch processing
- Export
Data is generated locally. It is entirely fictional and must not be used to impersonate a real person.
Data template
Choose a ready-made schema or build your own.
From 1 to 10,000 records.
Reuse the same seed to reproduce exactly the same dataset.
Fields to generate
These fields are used when the Custom template is selected.
Generated data
Copy or export the dataset in the selected format.
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How does this tool work?
Data Faker creates realistic fictional datasets for testing interfaces, APIs, imports and databases without using real personal information. Choose a template, locale, row count and output format. Generation runs entirely in your browser.
The tool uses a deterministic generation engine and localized catalogues. Each field is produced according to its type, including UUIDs, names, contact details, companies, dates, statuses and amounts. The Users, Customers and Orders templates provide coherent schemas that are ready to use.
An optional seed reproduces exactly the same dataset. Results can be serialized as JSON, CSV or SQL INSERT statements without being uploaded to a server. This makes it possible to create substantial demonstration datasets through a fast, private and repeatable local workflow.
Reproducibility depends on the seed, data locale, preset or selected fields and engine version. Save these settings with the test. An empty seed starts a new batch. This is a pseudo-random generator: its v4-shaped UUIDs must not replace cryptographic identifiers or secrets. The dedicated UUID generator uses a different source for independent random identifiers.
Values come from small demonstration catalogues. A city, postal code and country do not necessarily form a consistent or deliverable address. Do not contact the generated phone numbers or email addresses even when they look plausible. Records do not model relational database constraints: check foreign keys, uniqueness and business rules separately.
CSV uses commas and a header row. SQL produces simple INSERT statements and replaces non-alphanumeric characters in the table name with underscores. That cleanup is not identifier quoting for every SQL dialect; avoid reserved words and review the script before execution. Generation neither creates a table nor inserts anything into your database.
How to use this tool
Choose a template
Select Users, Customers, Orders or build a custom schema.
Configure generation
Set the volume, locale, format and an optional reproducible seed.
Export the result
Review the dataset, then copy or download the JSON, CSV or SQL output.
Use cases
Prepare automated tests
Create a reproducible fixture with a stable seed for development and QA scenarios.
Simulate an import
Generate CSV or JSON containing hundreds or thousands of rows before testing an import process.
Seed a demo database
Export SQL INSERT statements for prototypes, workshops and temporary environments.
Examples
Two reproducible JSON records
Custom preset, firstName and active fields, two rows and seed review. The input specifies the data locale; the output is not information about actual people.
Input
custom; count=2; locale=en; seed=review; fields=firstName,active; format=json; table=demo_dataResult
[
{
"firstName": "Ava",
"active": false
},
{
"firstName": "Ava",
"active": false
}
]The same sample as CSV
Identical generation settings yield identical values. Only serialization changes; booleans become true and false text in CSV cells.
Input
custom; count=2; locale=en; seed=review; fields=firstName,active; format=csv; table=demo_dataResult
firstName,active
Ava,false
Ava,falseThe same sample as SQL
The INSERT statements target demo_data without creating it. Check column names and schema types before running any script.
Input
custom; count=2; locale=en; seed=review; fields=firstName,active; format=sql; table=demo_dataResult
INSERT INTO demo_data (firstName, active) VALUES ('Ava', FALSE);
INSERT INTO demo_data (firstName, active) VALUES ('Ava', FALSE);Tips and best practices
- Use an explicit seed in automated tests so the same values are generated every time.
- Start with a small sample to validate the schema before generating thousands of rows.
- The records are fictional and should be used for testing and demonstrations, never to impersonate a real person.
Frequently asked questions
Do the records represent real people?
No. Identities and contact details are randomly assembled from demonstration catalogues and must not be treated as real information.
Can I reproduce exactly the same dataset?
Yes. Use the same seed, template, locale and row count to obtain the same result.
How many rows can I generate?
The tool supports from 1 to 10,000 records per run. For large volumes, validate the schema with a smaller sample first.
Does the data leave my browser?
No. Generation and JSON, CSV or SQL serialization are performed locally in the browser.
Are identifiers cryptographically random?
No. A seed makes generation predictable and repeatable. Use these values for tests; choose the dedicated UUID generator for independent random UUIDs.
Are addresses and contact details verified?
No. They are assembled from catalogues. A plausible appearance proves neither existence nor postal or administrative consistency. Never send messages or calls to these details.
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