Synthetic Data
Field types, locales, relational blueprints, imports and exports.
Realistic tabular, relational and time-series data for demos, tests and training — with no real records involved.
Field types
32+ generators across identity (names, gender, age), contact (email, phone), geography (address, city, state, postcode, country, lat/long), commerce (currency, price, SKU, category), temporal (date, datetime, timestamp, duration), technical (UUID, IP, MAC, URL, user agent, hash) and text (word, sentence, paragraph, enum, boolean).
Locales
| Locale | Names | Phone | Address |
|---|---|---|---|
| India | Regional first/last names | +91 10-digit | PIN codes, states |
| US | US census-style | +1 NPA-NXX | ZIP, states |
| EU | Mixed European | Country codes | Postcodes |
| Global | Mixed | E.164 | Generic |
Relational blueprints
- SaaS — users, workspaces, subscriptions, events.
- E-commerce — customers, products, orders, order_items.
- CRM — accounts, contacts, deals, activities.
- Analytics — sessions, pageviews, conversions.
- Support — tickets, messages, agents, SLAs.
Foreign keys stay consistent across tables and export as a ZIP with one file per table.
Getting a schema in
- AI schema — describe the dataset in English, get a validated schema.
- CSV header import — paste a header row, Nive infers types.
- SQL DDL import — paste
CREATE TABLEand keep your column names. - Saved schemas — private per user, reusable across runs.
Exports
CSV, JSON and SQL INSERT statements. Up to 5,000 rows per generation.