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 as CSV, JSON, SQL, XML and seven other formats.
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 22 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: CSV, JSON, Tab-Delimited, SQL (with 7 dialect options), Cassandra CQL, Firebase, InfluxDB, Custom delimited, Excel (CSV), XML, and DBUnit XML. 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. Credit card numbers pass the Luhn check and IBANs pass the MOD-97 check. 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 who need realistic non-production test data, 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 22 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 (10, 50, 100, 500 or 1000)
Choose your export format: CSV, JSON, Tab-Delimited, SQL, Cassandra CQL, Firebase, InfluxDB, Custom, Excel (CSV), XML, or DBUnit XML
Configure format-specific options (SQL dialect, table name, include headers, line endings, etc.)
Click 'Generate Data' to create your mock dataset
Copy to clipboard or download as a file for immediate use in your project
Frequently Asked Questions
How do I generate mock / fake data?
Add fields and pick a data type for each one — names, emails, addresses, phone numbers, dates, numbers, custom value lists and over 100 more. Choose how many rows to generate (10, 50, 100, 500 or 1000), pick an export format (CSV, JSON, SQL, XML and others) and click Generate Data. The tool produces realistic-looking but fake data. Copy or download it. Useful for: testing apps, seeding development databases, creating demo content. 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, country, zip), phone numbers, dates, numbers, credit-card numbers (random numbers that pass the Luhn check — see [Credit Card Validator](/tools/credit-card-validator/)), IBANs (valid check digits for GB, DE, FR, IT, ES and NL — the [Fake IBAN Generator](/tools/fake-iban-generator/) covers more countries), companies, URLs, IPs, UUIDs (see [UUID Generator](/tools/uuid-generator/)), Lorem Ipsum text, and industry types such as healthcare, automotive, e-commerce and gaming. The values come from built-in lists and random generators in this tool; there is no locale selection.
Is the data 'real' people / real businesses?
**No** — the data is generated at random and is not taken from any real person or business. Names are random combinations from short built-in lists; emails use domains such as example.com, test.com, demo.com, sample.org and mock.net; credit cards and IBANs pass their checksums but aren't tied to real accounts. Be careful anyway: only example.com is reserved for documentation, so the other domains are real, registered domains, and a random phone number or name could coincide with a real one. Don't send real email or SMS to generated addresses or numbers. The data is realistic-looking (passes basic format validation) but isn't designed to match any real entity.
Is my generation sent to a server?
No — generation runs entirely in your browser using the tool's own generator code. The fake data is generated locally; no server roundtrip. Verify in DevTools' Network tab: generating data makes no HTTP requests. Safe — even if your test data contains real-sounding companies or people, it was never sent anywhere.
Can I customize field types and rules?
Yes, within limits. Each field has its own type and name, and some types have options: min/max for numbers, decimals, date formats, or a custom list of values to pick from, plus a 'Blank Fields (%)' setting on every field to make a share of the values empty (null). You can also reorder fields by dragging. There are no cross-field rules: you can't restrict an email to one domain, limit countries, or make related records share the same user. For that kind of logic, use a data-generation library in your own code. For one-off mock generation with simple constraints, this tool is fast.
What format can I export?
Eleven formats. **CSV** and **Tab-Delimited**: for spreadsheet imports, database COPY operations, ETL testing. **JSON** (an object per line or an array): ideal for API mocks and JavaScript apps. **SQL INSERT** (MySQL, PostgreSQL, SQL Server, SQLite, Oracle, MariaDB, MongoDB), with optional CREATE TABLE, and **Cassandra CQL**. **XML** and **DBUnit XML**. **Firebase** and **InfluxDB**. **Custom**: your own delimiter and quote character. **Excel (CSV)** is a CSV file, not an .xlsx workbook. 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 should look real (test the typography, spacing), which this tool's built-in lists cover, though they are short and English-centric. For database performance testing: data distribution matters (don't use uniform random IDs — real data has skew); this tool generates uniformly random values, so use anonymized real data if distributions matter. 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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