Mock Data Generator & Dataset Builder
Build custom test datasets with names, dates, numbers and your own columns. Plan a repeatable schema and review export options and plan limits.
Your results appear here
Choose your settings and select Generate.
Scroll the table horizontally to see more columns. JSON and CSV downloads are included with Pro and Developer. Generated results are not stored.
Design the schema before the rows.
Test Data Builder creates a table with your own column names and supported field types. It is useful for import screens, data grids and API prototypes where a complete profile would include unnecessary fields. Begin with the smallest schema that can demonstrate the behavior you want to test.
- 01
Name each column
Use stable, distinct keys that match the receiving system. Choose the field type explicitly instead of assuming the column name controls generation.
- 02
Set meaningful bounds
For numbers and dates, choose a range that belongs in your scenario. For a choice field, enter the exact values your application accepts.
- 03
Inspect before scaling
Generate a small sample and verify field names, values and export interpretation. Increase the amount only after the contract is correct.
Example: a small order-import fixture
reference: UUID
customerEmail: Test email
quantity: Integer, 1–5
status: Choice, draft or readyThese are proposed column settings, not a connected order database. If an import needs a foreign key that matches another table, prepare that relationship explicitly in your own fixtures.
| Field | Test opportunity | Review |
|---|---|---|
| Integer / decimal | Bounds and rendering | Expected precision |
| Choice | Allowed status values | Exact spelling and case |
| Test email | Email-shaped fixture data | Outbound mail remains mocked |
Before you use the result
Practical answers for your next test.
Can I choose arbitrary column names?
Yes, within the editor rules. The selected field type determines the values; the label alone does not create custom behavior.
Will a preset restore my earlier results?
A seeded preset reproduces results while the generator and data versions match. Without a seed, it generates fresh rows. Keep an export if you need a permanent copy.
Why does CSV change a value in my spreadsheet?
Importers may infer numbers or dates. Set the intended column types and compare parsed values after import, particularly identifiers and leading zeros.