Generate realistic test data for development and QA testing with 175+ customizable field types.
Export in CSV, JSON, or Excel formats with up to 10,000 rows per dataset for comprehensive testing.
Perfect for developers, QA testers, and data scientists who need high-quality sample data instantly.
No fields added yet
Click 'Add Field' and configure column names with data types from names and emails to specialized fields
Set blank percentage to simulate real-world incomplete data patterns and edge cases for testing
Choose your row count (1-10,000) and export format, then generate and download your dataset instantly
Generate product catalogs with names, prices, SKUs, inventory counts, and supplier information for realistic store testing
Create user profiles with names, emails, phone numbers, addresses, and preferences for authentication and testing workflows
Generate transaction records with dates, amounts, account numbers, and categories for financial software testing
Create patient records with HIPAA-compliant mock data including medical codes, appointments, and contact information
Developers use mock data to populate databases, test user interfaces, and validate application logic before real data integration
QA teams generate comprehensive test datasets to verify application functionality, edge cases, and performance under various data conditions
Create large datasets to test database performance, query optimization, indexing strategies, and scalability limits
Generate sample JSON responses for API documentation, client development, and service integration testing
Mock data generation is the process of creating realistic but fake data that mimics the structure and patterns of real data. This enables developers to test applications without using sensitive or production data, ensuring privacy while maintaining development velocity.
High-quality mock data follows real-world patterns including proper formatting, statistical distributions, and realistic relationships between fields. This makes testing more meaningful and helps catch issues that simple random data might miss.
Modern mock data generators support hundreds of data types and can simulate complex business scenarios, making them essential tools for professional software development and testing workflows.
Using completely random data that doesn't follow real-world patterns
Choose appropriate data types and use realistic patterns that match your production data structure for more effective testing
Not setting blank percentages to simulate real-world data issues
Set realistic blank percentages (5-15% typical) to test how your application handles missing or incomplete data gracefully
Generating too little data for comprehensive testing
Generate diverse datasets with various edge cases and sufficient volume to test performance and identify scalability issues early
Not testing with different data quality scenarios
Create multiple datasets with varying completeness levels and data patterns to thoroughly test validation and error handling
Mock data is generated fake data that mimics real data patterns, while test data can include both real and generated data. Mock data ensures privacy and avoids using sensitive production information.
Yes, you can generate up to 10,000 rows per dataset. For larger testing needs, create multiple files and combine them, or use the tool in batches to generate comprehensive test data.
Our generator uses realistic patterns based on actual data distributions. Names follow cultural naming patterns, dates are logically distributed, and numerical values follow expected ranges for each data type.
The generated data is completely fake and doesn't contain any real personal information, making it safe for development, testing, and demonstration purposes without privacy concerns.
Yes, each field type supports various customization options including value ranges, formats, and patterns. You can also set blank percentages to simulate real-world data incompleteness.
Review generated samples before full generation, use appropriate data types, set realistic blank percentages, and validate that the patterns match your expected production data structure.