Random data generator for test datasets and prototypes. Choose fields and a row count, generate sample records, and review the output before exporting.
The Random Data Generator creates sample records for testing, prototypes, and demos. Use it as a mock data generator for names, emails, addresses, identifiers, and other fields instead of copying production records.
Review generated values before use. Random data can resemble real information by coincidence, so treat the output as test data rather than verified contact or payment details.
Choose how many rows you want (1 to 10,000), pick the fields you need from the catalogue, and click Generate. The output appears as a table you can browse, with one-click export to CSV, JSON, SQL INSERT statements, or TSV. Generate again when you need another sample.
Personal: first name, last name, full name, gender, date of birth, age
Contact: email address, phone number (international or country-specific), website URL
Address: street address, city, state / region, postal code, country, latitude & longitude
Internet: username, password, IPv4 address, IPv6 address, MAC address, user-agent string
Business: company name, job title, department, SKU, EAN-13 barcode
Finance: credit-card test number (Visa / MasterCard / Amex), CVV, expiry, IBAN, BIC, currency code, transaction amount
Dates & times: random date in a range, ISO timestamp, Unix timestamp, weekday
Identifiers: UUID v4, GUID, MongoDB ObjectId, short ID, slug
Text: lorem ipsum sentences, paragraphs, words, slugs
Custom: integer in range, float in range, pick from a list, boolean
Choose fields and their order, set the row count, and review the available locale and seed options. Use the same settings and seed when you need to compare repeated test runs. Preview the generated rows before exporting them.
Select fields from the catalogue (drag to reorder if you care about column sequence).
Set the row count (default 100).
Optionally pick a locale or paste a seed for reproducible output.
Click Generate.
Browse the preview, then Export to CSV / JSON / SQL / TSV, or Copy to clipboard.
Use sample records for these development tasks:
Database seeding: populating users, orders, products tables in dev / staging environments.
API testing: generating payloads for Postman / Bruno collections without exposing production data.
UI & design demos: compare a 500-row table of sample names and addresses with labels such as "User 1, User 2, User 3".
QA automation: deterministic seeded data for repeatable test cases.
Sales demos: showing a product with believable customer data without leaking real customers.
Form testing: pasting realistic emails / addresses into signup forms to verify validation.
Sample analytics screenshots: sharing dashboard mockups in blog posts or marketing decks without redacting real user data.
Training datasets: synthetic data for tutorials, courses, or workshops on data engineering and analytics.
Generated records are intended for development and demonstrations. Do not use them as real delivery addresses, contacts, account credentials, or payment details.
Choose a locale that fits the test or demo. Review the names, addresses, and formatting in the preview rather than assuming they meet every local rule.
If you use seed mode, keep the seed, field selection, and row count together with the test. Compare the generated output before relying on it for a regression test.
No. Treat them as sample data. The existing example uses example.com; do not send messages to generated addresses without verifying the destination.
Use payment data only in a test environment approved by your payment provider. The existing example, 4111 1111 1111 1111, is test data, not a card to use for a real purchase. A checksum match alone does not establish that a number is suitable for a payment test.
No. Treat generated addresses as sample data, not confirmed delivery destinations. A generated value may resemble a real address.
Up to 10,000 rows per click. Need more? Re-run with a different seed and concatenate the results. Larger sets may take a few seconds and use noticeable browser memory.
Use the same seed string, fields, and row count in seed mode. Compare the output before using it in a repeatable test.
Faker.js is a library used in code. This tool provides an interface for choosing fields and exporting a dataset without writing a script.
The listed export formats are CSV, JSON, SQL INSERT, TSV. For YAML or XML, generate JSON and use the JSON to YAML converter or JSON to XML converter.
How to Use Random Data Generator - Mock Data for Testing
Select the data fields for your test and arrange their order. Check that the selected fields match the destination schema.
Choose the row count and locale. If you need to repeat the dataset, keep the seed value and settings with your test.
Click Generate. Review the sample rows and treat contact, address, and payment fields as test data, not verified real-world information.
Choose an available export format or copy the output. Check the file against the schema expected by your test or prototype.
Random data generator for test datasets and prototypes. Choose fields and a row count, generate sample records, and review the output before exporting.
Tool Use:
454Added:
Jun 10, 2026Type:
Free ToolPrivacy:
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