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Build a custom schema row by row, generate up to 1,000 realistic records with faker.js v8, then preview and export them as JSON, CSV or SQL INSERT.

📘 How to Use

  1. Set the field names and data types.
  2. Choose the number of rows and the output format (JSON/CSV/SQL).
  3. Click Generate, check the preview, then copy or download.

Dummy Data Generator

Schema Definition

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Generate Realistic Mock Data | Streamline Your Development Workflow

Stop manually typing "test1" and "test2" into your database. Generate structured, production-like datasets for API mocking, database seeding, and UI stress testing in seconds.

💡 About This Tool

High-quality applications require data that mimics the real world. This tool lets you build a custom schema field by field and generate hundreds of rows without writing a single line of code. Whether you need a quick JSON response for the frontend or SQL inserts to populate a dev environment, it handles the heavy lifting.

  • Define Flexible Schemas: Map custom keys (like user_id or created_at) to realistic data types including UUIDs, full names, emails and phone numbers.
  • Export to Multiple Formats: JSON for REST and GraphQL API mocks, CSV for spreadsheet analysis and bulk imports, or standard INSERT INTO statements for database population.
  • Preview Before You Export: Check the first 10 rows in a live table before downloading the full dataset, so a wrong schema never costs you a re-run.
  • Generate at Scale: Produce up to 1,000 rows per batch while keeping the browser responsive.

🧐 Frequently Asked Questions

How many rows can I generate at once? You can generate up to 1,000 rows per batch. The cap keeps generation fast and the preview table responsive inside the browser.

Can I use the generated data in commercial projects? Yes. Every value is produced procedurally, so the output is free to use for any purpose, including commercial software testing.

Will special characters break the SQL output? No. Single quotes inside string values are escaped automatically (' becomes ''), so the generated INSERT statements run without syntax errors.

📚 Why High-Quality Mock Data Matters

Using "garbage" text often masks UI bugs. Real-world data helps you catch layout breaks caused by long names, or logic errors triggered by specific date formats, early in the development cycle. By testing against realistic distributions you build more resilient applications — and you sidestep the temptation to copy production records, with real customer details, into a development environment.