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Excel to SQL Converter (Free AI Tool)
Convert Excel spreadsheets to SQL with automatic CREATE TABLE and INSERT statements. Transform XLSX data to MySQL, PostgreSQL, or SQLite with intelligent type inference. Import spreadsheets to relational databases without manual SQL writing.
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Hint: Select conversion type, paste your data, and get instant conversion. Supports JSON, YAML, XML, Excel, PDF, and more.
How It Works
- 1
Upload Excel File with Headers
Select your Excel file where the first row contains column headers. These headers become SQL column names. Supports .xlsx, .xls formats and multiple sheets.
- 2
AI Infers SQL Types from Cells
The tool analyzes cell values to determine SQL data types. Numbers become INT or DECIMAL, text becomes VARCHAR, dates convert to TIMESTAMP, booleans become BOOLEAN. Creates CREATE TABLE with appropriate constraints.
- 3
Copy SQL CREATE and INSERT Statements
Receive SQL with CREATE TABLE definition followed by bulk INSERT statements. Ready to execute in MySQL, PostgreSQL, SQLite, or SQL Server for immediate data import.
Excel vs SQL: Data Import Comparison
| Feature | Excel | SQL |
|---|---|---|
| Format | Binary spreadsheet | Text SQL script |
| Structure | Rows and columns | CREATE TABLE + INSERT |
| Data Types | Cell formats | SQL types (INT, VARCHAR, DATE) |
| Formulas | Calculated values | Static values only |
| Use Case | Desktop analysis | Relational database storage |
| Multi-sheet | Multiple tabs | Multiple tables |
Code Examples
Example 1: Product Spreadsheet to SQL Table
ProductID, Name, Price, Stock
P001, Laptop, 999.99, 50
P002, Mouse, 29.99, 200
P003, Keyboard, 79.99, 150CREATE TABLE products (
ProductID VARCHAR(10) PRIMARY KEY,
Name VARCHAR(50) NOT NULL,
Price DECIMAL(10,2) NOT NULL,
Stock INT NOT NULL
);
INSERT INTO products (ProductID, Name, Price, Stock) VALUES
('P001', 'Laptop', 999.99, 50),
('P002', 'Mouse', 29.99, 200),
('P003', 'Keyboard', 79.99, 150);Key Changes:
Excel headers (ProductID, Name, Price, Stock) become SQL column names. The converter infers VARCHAR for alphanumeric IDs, VARCHAR for text names, DECIMAL for prices with decimals, and INT for whole number stock counts. The first column is designated as PRIMARY KEY. Each Excel row converts to an INSERT VALUES clause. Strings are properly quoted, numbers are unquoted, and appropriate SQL syntax ensures compatibility with MySQL, PostgreSQL, and SQLite.
Example 2: Employee Data with Dates
EmployeeID, FullName, Department, HireDate, Active
101, John Smith, Engineering, 2024-01-15, TRUE
102, Jane Doe, Marketing, 2023-11-20, TRUE
103, Bob Wilson, Sales, 2024-02-01, FALSECREATE TABLE employees (
EmployeeID INT PRIMARY KEY,
FullName VARCHAR(100) NOT NULL,
Department VARCHAR(50) NOT NULL,
HireDate DATE NOT NULL,
Active BOOLEAN NOT NULL
);
INSERT INTO employees (EmployeeID, FullName, Department, HireDate, Active) VALUES
(101, 'John Smith', 'Engineering', '2024-01-15', TRUE),
(102, 'Jane Doe', 'Marketing', '2023-11-20', TRUE),
(103, 'Bob Wilson', 'Sales', '2024-02-01', FALSE);Key Changes:
Type inference handles multiple data types correctly. Integer EmployeeID becomes INT PRIMARY KEY. Text names and departments become VARCHAR with inferred lengths. Excel dates (2024-01-15) convert to SQL DATE type with proper formatting. Boolean TRUE/FALSE values become SQL BOOLEAN type. The generated SQL follows standard conventions with NOT NULL constraints for required fields. This schema is immediately usable in any relational database management system.
Frequently Asked Questions
The converter analyzes cell values to infer SQL types: numbers become INT or DECIMAL based on decimals, text becomes VARCHAR with length from longest value, dates convert to DATE or TIMESTAMP, and TRUE/FALSE becomes BOOLEAN. Empty cells are handled as NULL. Mixed-type columns default to VARCHAR for safety.
No. All Excel to SQL conversion happens entirely client-side in your browser using JavaScript. Your spreadsheet data never leaves your machine. Files are processed in memory and discarded after conversion.
Yes. Multiple sheets generate separate CREATE TABLE statements with sheet names as table names. Large files with thousands of rows generate efficient bulk INSERT statements. For very large datasets, consider CSV intermediate format or batch processing for optimal database import performance.