Mock Data Generator
Build custom schemas and generate thousands of realistic fake records as JSON, CSV, SQL, or TSV using Faker.js with locale support.
How to Use
Generate realistic test data in four steps with our visual schema builder:
- Define your schema: Add fields to the schema builder table. Each field has a name and a data type selected from 60+ categories including person names, addresses, commerce, finance, and system identifiers. Use quick presets like "User Table" or "Orders" to load common schemas instantly.
- Configure generation: Set the number of rows (1 to 10,000), choose a locale for region-specific data like Japanese names or German addresses, and select your export format: JSON, CSV, SQL INSERT statements, or TSV.
- Generate data: Click the Generate button. Faker.js runs entirely in your browser to produce realistic, varied data. The first 5 rows appear in a table preview so you can verify the output before exporting.
- Export results: Copy the full output to your clipboard or download it as a file. SQL mode generates ready-to-execute INSERT INTO statements with your specified table name.
About This Tool
What Is a Mock Data Generator?
A mock data generator creates synthetic datasets that mimic the statistical properties and format of real-world data without containing actual personal information. Unlike random string generators, mock data tools produce contextually valid values: email addresses with proper formatting, phone numbers with correct digit counts, prices with realistic decimal precision, and addresses with real city-state-zip combinations.
This tool uses Faker.js v10, the most widely adopted fake data library in the JavaScript ecosystem with over 5 million weekly npm downloads. Faker.js maintains curated datasets for 60+ locales, ensuring that generated names, addresses, and phone numbers follow locale-specific conventions. All generation runs client-side in your browser — no data is transmitted to any server, making it safe for schema prototyping on confidential projects.
Supported Field Categories
The schema builder exposes 60+ data types organized into logical categories. Person fields generate first names, last names, full names, job titles, and bios. Internet fields produce emails, usernames, URLs, IPv4/IPv6 addresses, MAC addresses, and user agent strings. Location covers street addresses, cities, states, zip codes, countries, and GPS coordinates. Commerce generates product names, prices, departments, and ISBNs. Finance creates account numbers, amounts, currency codes, credit card numbers, IBANs, and BIC/SWIFT codes. Date fields produce past, future, recent, and birthdate values. System generates UUIDs, file names, MIME types, and semantic version numbers. Custom types let you specify literal values, pick from comma-separated lists, or generate sequential row numbers.
Export Formats
JSON produces a formatted array of objects, ready for API mocking, frontend development, and test fixtures. CSV generates RFC 4180 compliant comma-separated output with proper quoting and escaping. SQL creates INSERT INTO statements with correct value quoting for strings, numbers, booleans, and NULLs. TSV produces tab-delimited output compatible with spreadsheet applications and data pipelines.
Why Use This Tool
Why Use This Tool?
Testing with realistic data exposes edge cases that hard-coded fixtures miss. Accented characters in names reveal encoding bugs. Long product descriptions overflow UI containers. Negative financial amounts break unsigned integer columns. A mock data generator with diverse field types surfaces these issues during development, before users encounter them in production.
Competitors like Mockaroo limit free users to 1,000 rows and require authentication. Our generator runs unlimited batches up to 10,000 rows with no signup, no ads inside the workspace, and zero server contact. The visual schema builder lets you prototype database tables faster than writing seed scripts, while preset templates for users, products, orders, and employees cover the most common testing scenarios.
Need to test internationalization? Switch the locale to Japanese, German, or Portuguese and verify that your UI handles non-Latin scripts, longer address formats, and locale-specific phone number patterns. Combine this tool with our JSON Formatter, SQL Formatter, or CSV Viewer for a complete data pipeline workflow.