Free Online SQL Data Generator — Mock INSERT Statements

Define your table schema and generate realistic mock INSERT data with one click. Supports 15+ data types, automatic name/email/price detection, and up to 1,000 rows at a time. 100% free, no signup, all processing in your browser.

Generate Mock SQL Data Now — Free

Why You Need an SQL Data Generator

Writing INSERT statements by hand is tedious. For a schema with ten columns, you spend more time typing quotes and commas than the data itself is worth. When you need 50 rows to populate a development database or seed integration tests, manual INSERTs are not just slow — they are error-prone. One missing quote and your script fails at runtime.

A free online SQL data generator solves this by letting you describe your schema and automatically producing syntactically correct INSERT statements with realistic test data. A column named email generates addresses like alice.smith@example.com. A column named price generates dollar amounts between $10 and $1,000. A UUID column gets a valid xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx string. The result is data that looks believable in demos, documentation, and staging environments.

Every developer has at least one story about writing a script to populate a test database, only to spend more time debugging the script than it would have taken to write INSERTs manually. This generator eliminates that overhead entirely.

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Schema Presets

Pre-built schemas for Users, Products, Orders, and Employees. Load one and customize, or start from scratch.

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Smart Data Detection

Column names like email, phone, price, city, and name auto-generate realistic values. No configuration needed.

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Up to 1,000 Rows

Generate anywhere from 1 to 1,000 INSERT statements in a single click. Perfect for seeding test databases.

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15+ Data Types

INT, VARCHAR, TEXT, BOOLEAN, DATE, TIMESTAMP, DECIMAL, FLOAT, UUID, JSON, ENUM, and more.

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One-Click Copy

Generated INSERTs are ready to paste into your SQL client, migration script, or seed file.

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100% Client-Side

All processing runs in your browser. Your schema and data never leave your device.

How to Generate Mock SQL Data

  1. Enter your table name — e.g. employees, products, orders
  2. Define columns — add column names and pick a data type (INT, VARCHAR, DECIMAL, DATE, BOOLEAN, etc.)
  3. Set row count — how many INSERT rows to generate (1 to 1,000)
  4. Click Generate — the tool produces complete INSERT statements with realistic data
  5. Copy the result — paste directly into your database console or seed file

The generator also includes an Options field where you can provide hints — like price, email, or name — to guide what kind of mock data each column produces. Most columns detect this automatically from the column name, but the hint field lets you override the default behavior.

Example Use Cases

Seeding a Development Database

When you set up a new project locally, you need sample data to verify that queries, views, and API endpoints work correctly. Manually INSERTing 30 rows into 5 tables is an afternoon killer. The generator gives you usable data in seconds.

-- Define: table=employees, 5 rows -- Columns: first_name(VARCHAR), last_name(VARCHAR), email(VARCHAR), salary(DECIMAL), department(VARCHAR), hire_date(DATE) INSERT INTO employees (first_name, last_name, email, salary, department, hire_date) VALUES ('Alice', 'Smith', 'alice.smith@gmail.com', 85342.18, 'Engineering', '2025-03-14'); INSERT INTO employees (first_name, last_name, email, salary, department, hire_date) VALUES ('Bob', 'Johnson', 'bob.johnson@outlook.com', 62471.50, 'Sales', '2024-08-22'); INSERT INTO employees (first_name, last_name, email, salary, department, hire_date) VALUES ('Grace', 'Williams', 'grace.williams@company.com', 91205.99, 'Marketing', '2025-11-03'); INSERT INTO employees (first_name, last_name, email, salary, department, hire_date) VALUES ('Jack', 'Brown', 'jack.brown@proton.me', 51200.00, 'Support', '2024-02-11'); INSERT INTO employees (first_name, last_name, email, salary, department, hire_date) VALUES ('Mia', 'Davis', 'mia.davis@gmail.com', 120000.00, 'Engineering', '2023-06-30');

Populating an E-Commerce Schema

Testing an order system requires related products, customers, and orders tables. The generator handles each table individually, and you can control foreign key IDs by setting the right column type and row count.

-- Products table - 8 rows INSERT INTO products (product_name, category, price, stock, is_available) VALUES ('Pro Widget', 'Electronics', 499.99, 150, TRUE); INSERT INTO products (product_name, category, price, stock, is_available) VALUES ('Slim Device', 'Home', 29.95, 2000, TRUE); INSERT INTO products (product_name, category, price, stock, is_available) VALUES ('Premium Kit', 'Sports', 89.00, 45, TRUE);

Supported Data Types and What They Generate

Data Type Generated Value
INTRandom integer 1–9,999
BIGINTRandom integer 10,000–99,999,999
VARCHARName, email, city, phone, or company — detected from column name
TEXTShort descriptive sentence
BOOLEANTRUE or FALSE
DATEDate in YYYY-MM-DD format within a 2-year range
TIMESTAMPTimestamp in YYYY-MM-DD HH:MM:SS format
DECIMALPrice or amount, e.g. 49.99, 85342.18
FLOATRandom decimal with 4-digit precision
UUIDValid UUID v4 string
JSONEmpty JSON object {}
ENUMvalue_a, value_b, value_c (or custom values from Options)
SERIALDEFAULT (database auto-increment)

Columns ending in _id generate integers suitable for foreign key references. Columns named password, hash, or token generate hex strings that look like real authentication values.

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