Mock Data / Fake Data Generator

Need test data for your app, database, or demo? This tool generates realistic fake data on demand: names, emails, phone numbers, addresses, UUIDs, dates, and more. Choose your fields, set the row count, and export as JSON, CSV, or SQL INSERT statements.

How it works

Add the fields you need from the dropdown, set the number of rows (up to 500), pick your output format, and click Generate. Every generation uses a fresh random seed so results differ each time. All processing happens in your browser. Nothing is sent to a server.

Available field types

First/last/full names, email addresses, phone numbers, street addresses, cities, states, ZIP codes, countries, company names, job titles, UUIDs, integers, floats, booleans, dates, URLs, usernames, hex colors, IP addresses, fake credit card numbers, and lorem ipsum paragraphs.

Output formats

JSON: an array of objects, ready for your API mocks or fixtures
CSV: importable into spreadsheets, databases, or ETL pipelines
SQL: a ready-to-run INSERT INTO statement for any relational database

Why mock data matters

Testing with placeholder data like "foo" and "bar" catches fewer bugs than realistic data. Real-world data contains edge cases that synthetic placeholders miss: names with apostrophes (O'Brien), addresses with apartment numbers, emails with subdomains, and phone numbers in various formats. Realistic mock data reveals these issues before production does.

Seeding for reproducibility

Using a fixed random seed produces the same dataset every time it is run. This is useful for regression tests and snapshot testing - if the seed is pinned, the generated data is deterministic, so test failures indicate actual behavior changes rather than random variation.

Data privacy best practice

Never use real user data for development, testing, staging, or demos. Regulations including GDPR (Europe) and CCPA (California) impose strict rules on where and how personal data may be processed. Using synthetic mock data eliminates this risk entirely and avoids accidental exposure of PII in logs, screenshots, or demo environments.

SQL output guide

The SQL output generates standard INSERT INTO statements compatible with most relational databases:

SQLite / PostgreSQL / MySQL: use the output directly.
SQL Server: may require wrapping string values with N'' prefix for Unicode support.
Table name: defaults to mock_data - edit the SQL before running if your table has a different name.
NULL values: boolean and optional fields may output NULL - ensure your schema allows nullable columns.