Mock Data Generator
Generate realistic mock data for testing, prototyping, and development. Create custom schemas with multiple data types and export in various formats.
Field Schema
CSV Options
Generated Data
Generated data will appear here
Configure your schema and click "Generate Data" to start
About Mock Data Generator
Privacy-First: All data generation happens in your browser. No data is sent to any server.
Versatile Output: Export data in JSON, CSV, SQL, XML, or Array format for use in any application.
Realistic Data: Generate realistic mock data for testing, prototyping, and development.
Custom Fields: Create custom schemas with unlimited fields and data types.
About Mock Data Generator
The Mock Data Generator is a powerful, free online tool that creates realistic test data with 106+ data types across 20 industry-specific categories. Generate comprehensive mock datasets including personal data (names, emails, addresses, phone numbers), financial data (credit cards, IBAN, SWIFT codes, bank accounts), healthcare data (diagnoses, medications, blood types, insurance), e-commerce data (products, SKUs, orders, payments), automotive data (VINs, license plates, car models), education data (courses, degrees, GPAs, universities), social media metrics (followers, likes, engagement), real estate listings (properties, features, square footage), gaming data (titles, achievements, scores), and much more. Export your generated data in 11 different formats including JSON, CSV, SQL (with 7 dialect options), XML, DBUnit XML, Firebase, InfluxDB, Cassandra CQL, Excel, and custom delimited formats. Perfect for database seeding, API testing, application prototyping, and development without using real user information.
Why use a Mock Data Generator?
Mock data generation is critical for modern software development, testing, and prototyping. This tool provides industry-specific realistic test data that helps developers test edge cases, validate business logic, and demonstrate features safely. With 106 customizable data types spanning finance, healthcare, e-commerce, education, automotive, transportation, gaming, and more, you can create complex, realistic datasets instantly. Features include per-field blank percentage control for testing null handling, auto-increment IDs, drag-and-drop field reordering, SQL dialect support (MySQL, PostgreSQL, SQL Server, SQLite, Oracle, MariaDB, MongoDB), format-specific export options, and a searchable modal interface with category filtering. Generate data that passes validation checks while maintaining complete privacy and GDPR compliance. All processing happens in your browser - no data is sent to any server.
Who is it for?
Essential for full-stack developers building and testing applications, QA engineers creating comprehensive test datasets, database administrators seeding development and staging databases, API developers testing endpoints with realistic data, frontend developers building UI mockups and demos, backend engineers populating databases for load testing, DevOps engineers setting up test environments, data scientists creating sample datasets, product managers demonstrating features, fintech developers testing payment systems, healthcare software developers complying with HIPAA requirements, e-commerce platform developers testing checkout flows, educational software developers, game developers testing leaderboards and achievements, and any developer needing realistic mock data without using production information.
How to use the tool
Click 'Add Field' to create your custom data schema
Click on the 'Data Type' button to open the modal with 106+ data types organized into 20 categories
Search or filter by category (Finance, Healthcare, E-commerce, Gaming, etc.) to find the perfect data type
Configure field-specific options like min/max values, date formats, or blank percentage
Drag fields using the grip icon to reorder your schema
Set the number of rows to generate (1-10,000)
Choose your export format: JSON, CSV, SQL, XML, Firebase, InfluxDB, or Custom
Configure format-specific options (SQL dialect, include headers, line endings, etc.)
Click 'Generate Data' to create your mock dataset instantly
Copy to clipboard or download as a file for immediate use in your project
Frequently Asked Questions
How do I generate mock / fake data?
Define the data structure (fields and types) — names, emails, addresses, phone numbers, dates, numbers, custom enums. Choose how many records to generate (1-1000+ typically). Click Generate and the tool produces realistic-looking but fake data in JSON, CSV, or SQL INSERT statement format. Copy or download. Useful for: testing apps, seeding development databases, creating demo content, training ML models. Runs entirely in your browser.
What types of fake data can I generate?
Common types. Names (first, last, full), emails (random `firstname.lastname@domain.com`), addresses (street, city, state, country, zip), phone numbers (with country/region formatting), dates (in any range), credit-card numbers (test PANs — see [Credit Card Validator](/tools/credit-card-validator/)), IBANs (use [Fake IBAN Generator](/tools/fake-iban-generator/)), companies, URLs, IPs, UUIDs (see [UUID Generator](/tools/uuid-generator/)), Lorem Ipsum text. Most tools use Faker.js or similar libraries for realistic locale-aware generation.
Is the data 'real' people / real businesses?
**No** — all generated data is fake. Names are random combinations from common name pools; emails are synthetic; addresses point to non-existent locations; phone numbers don't connect to real people; IBANs and credit cards pass checksums but aren't real accounts. Safe for testing — you can't accidentally email a real person or charge a real card. The data is realistic-looking (passes basic format validation) but doesn't correspond to any real entity.
Is my generation sent to a server?
No — generation runs entirely in your browser via Faker.js or similar libraries. The fake data is generated locally; no server roundtrip. Verify in DevTools' Network tab: zero HTTP requests. Safe — even if your test data contains references to real-sounding companies or people, they were never sent anywhere.
Can I customize field types and rules?
Yes — most mock data tools let you specify per-field rules: 'first_name (any)', 'email (gmail.com only)', 'age (18-65)', 'country (US or UK only)', 'created_at (last 30 days)'. For more complex logic (conditional field generation, related records — same user appears in multiple records), use a library directly: Faker.js (Node), Faker (Python), Bogus (.NET). For one-off mock generation with simple constraints, this tool is fast.
What format can I export?
Common formats. **JSON**: ideal for API mocks, JavaScript apps, MongoDB. **CSV**: for spreadsheet imports, database COPY operations, ETL testing. **SQL INSERT**: paste directly into your database. **TypeScript / JavaScript code**: array literal for embedding in tests. Some tools also support JSON Schema or OpenAPI sample-data generation. Pick the format matching your destination.
When should I use mock data vs anonymized real data?
**Mock data**: for early development (before real data exists), unit/integration testing, demos to clients, ML model training where data variety matters more than authenticity. **Anonymized real data**: for performance testing (real-data distributions matter), production-replica QA environments, machine-learning models where edge cases matter. The risk with anonymized data: re-identification through correlation (de-anonymization attacks). For privacy-critical use, fully-synthetic mock data is safer.
How realistic does the data need to be for testing?
Depends on the test. For UI/UX testing: names and addresses must look real (test the typography, spacing); use this tool's locale-aware generation. For database performance testing: data distribution matters (don't use uniform random IDs — real data has skew); use realistic distributions or anonymized real data. For ML model training: data must match production distribution — synthetic data may not capture edge cases. Match the data quality to the test's needs.
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