Fake Data Generator: Create Realistic Test Data for Development and Demos

Testing with test1@test.com and John Doe gives you false confidence. Realistic test data reveals edge cases — names with accents, long email addresses, international phone formats, addresses with apartment numbers. A fake data generator produces believable data at scale.

What Is Fake Data Generator?

A fake data generator creates realistic but fictitious data for any schema you define. It supports common types (names, emails, dates, addresses, phone numbers, UUIDs) and custom types (product names, company names, credit card numbers, IP addresses). Output formats include JSON, CSV, SQL INSERT, and TypeScript.

How to Use Fake Data Generator on DevToolHub

1. Open the Fake Data Generator tool on DevToolHub — no signup required.
2. Define your data schema: field names and types.
3. Set the number of records to generate (1 to 10,000+).
4. Configure locale-specific formats (US, EU, UK, etc.) if needed.
5. Choose the output format: JSON array, CSV, SQL INSERT statements, or TypeScript.
6. Generate and download or copy the data.

Generating User Test Data

Create 5 realistic user records:

[
  {
    "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
    "name": "Marguerite Beaumont",
    "email": "m.beaumont@outlook.com",
    "phone": "+1 (415) 555-0142",
    "address": "742 Evergreen Terrace, Apt 3B",
    "city": "San Francisco",
    "state": "CA",
    "zip": "94102",
    "role": "editor",
    "createdAt": "2024-02-14T09:23:17Z"
  },
  // ... 4 more unique records
]

Each record has a unique UUID, realistic name, properly formatted phone number, and valid-looking address.

SQL INSERT Statements

Generate ready-to-run SQL for seeding a database:

INSERT INTO users (id, name, email, role, created_at) VALUES
  ('a1b2c3d4...', 'Marguerite Beaumont', 'm.beaumont@outlook.com', 'editor', '2024-02-14T09:23:17Z'),
  ('b2c3d4e5...', 'James O''Sullivan', 'james.osullivan@gmail.com', 'viewer', '2024-03-01T14:08:55Z'),
  ('c3d4e5f6...', 'Yuki Tanaka', 'y.tanaka@company.co.jp', 'admin', '2024-01-22T06:45:30Z'),
  ('d4e5f6a7...', 'Fatima Al-Hassan', 'f.alhassan@proton.me', 'editor', '2024-04-10T11:17:42Z'),
  ('e5f6a7b8...', 'Björn Lindström', 'bjorn.l@telia.se', 'viewer', '2024-02-28T16:33:09Z');

Notice the diverse names, proper SQL escaping (O'Sullivan → O''Sullivan), and international email domains.

E-Commerce Product Data

Generate product records for a store demo:

[
  {
    "sku": "ELEC-2024-0847",
    "name": "Wireless Noise-Canceling Headphones",
    "category": "Electronics",
    "price": 129.99,
    "stock": 342,
    "rating": 4.6,
    "reviews": 1847
  }
]

Custom schemas let you define business-specific fields with realistic value ranges.

Pro Tips

• Test edge cases — include names with apostrophes, umlauts, and long strings to stress-test your UI.
• Match your schema exactly — generate data that matches your database column types and constraints.
• Use consistent locales — don't mix US phone formats with UK postcodes in the same dataset.
• Scale gradually — start with 100 records for development, then 10,000 for performance testing.

When You Need This

• Seeding development and staging databases with realistic data
• Creating demo data for client presentations and sales demos
• Populating UI prototypes with believable content
• Load-testing applications with thousands of realistic records