Test Data Generator
Generate realistic fake records for development, unit tests, and database seeding. Pick your fields, choose JSON, CSV, SQL, or TypeScript output, then download or copy instantly — entirely in your browser, no server required.
Frequently Asked Questions
Is the generated data safe to use in production?▼
No — test data is intentionally fake and should only be used in development, staging, or automated test environments. Never seed a production database with generated records, as names, emails, and phone numbers are fabricated from static arrays and do not belong to real people.
Does this tool send data to any server?▼
No. All data generation happens entirely in your browser using a seeded pseudo-random number generator (PRNG). No network requests are made, nothing is logged, and no generated data ever leaves your machine.
What is the password field format?▼
The password field generates a bcrypt-style placeholder string in the format $2b$NN$<53-char-hash>. This mimics the output of real bcrypt hashing libraries (like bcryptjs or passlib) but is not a real hash — it is just a realistic-looking placeholder for UI and schema testing.
Why does the CSV output expand address into multiple columns?▼
CSV and SQL formats require flat key-value rows with no nested objects. When the address field is selected, it is automatically expanded into four separate columns: address_street, address_city, address_state, and address_zip. JSON and TypeScript outputs preserve the nested address object.
How do I use the SQL output in my database?▼
The SQL output generates a single INSERT INTO users (...) VALUES ... statement. Copy it directly into your database client (psql, MySQL Workbench, DBeaver, etc.) and run it. You may need to rename the table or column names to match your actual schema. Column types should be VARCHAR or TEXT for most fields, INTEGER for age.
Can I generate more than 100 records?▼
The tool caps at 100 records per generation to keep the browser UI responsive. If you need thousands of records, generate in multiple batches and concatenate the output files, or use a server-side library like Faker.js (Node.js) or Faker (Python) for large-scale datasets.
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