SQLify

A lightweight web application that transforms CSV and Excel files into SQL code automatically. Upload your data files and generate ready-to-use CREATE TABLE and INSERT INTO statements.

Features

• Multi-format Support: Upload CSV and Excel files
• Table Customization: Rename your database table before generating SQL
• Column Selection: Choose which columns to include or exclude from the output
• Complete SQL Generation:
  • CREATE TABLE statements with proper column definitions
  • INSERT INTO statements for all data rows
• Modern Interface: Clean, centered, and responsive design
• Copy & Download: Easy copy-to-clipboard or download SQL files
• Data Validation: Handles empty cells, trims whitespace, and removes empty rows
• Real-time Preview: See your SQL output update as you make changes

Installation & Setup

Prerequisites

• Node.js (version 14 or higher)
• npm or yarn package manager

Steps

1. Clone the repository

   git clone https://github.com/vincenzo113/sqlify.git
   cd sqlify

2. Install dependencies

   npm install

3. Start the development server

   npm start

4. Open your browser Navigate to http://localhost:3000

Dependencies

The project uses the following main libraries:

• React: Frontend framework
• Papa Parse: CSV parsing and processing
• SheetJS: Excel file reading and parsing

Usage Guide

Step 1: Upload Your File

1. Click on "Choose File" or drag and drop your CSV/Excel file
2. Supported formats: .csv, .xlsx

Step 2: Review Generated SQL

• The app automatically generates SQL code in the text area
• CREATE TABLE statement
• INSERT INTO statements for your data

Step 3: Customize (Optional)

• Rename Table: Modify the table name in the generated SQL
• Select Columns: Use the column selector to include/exclude specific columns
• Changes update the SQL output in real-time

Step 4: Export Your SQL

• Copy: Click "Copy" to copy SQL to clipboard
• Download: Click "Download" to save as .sql file

Alternative: Paste Data

• Click "Paste" to manually input CSV or Excel data
• Useful for quick conversions without file uploads

Example Input/Output

Sample CSV Input

id,name,email,age,created_date
1,John Doe,[email protected],28,2023-01-15
2,Jane Smith,[email protected],34,2023-01-16
3,Bob Johnson,[email protected],25,2023-01-17

Generated SQL Output

CREATE TABLE my_table (
    id VARCHAR,
    name VARCHAR(,
    email VARCHAR,
    age VARCHAR,
    created_date VARCHAR
);

INSERT INTO my_table (id, name, email, age, created_date) VALUES
(1, 'John Doe', '[email protected]', 28, '2023-01-15'),
(2, 'Jane Smith', '[email protected]', 34, '2023-01-16'),
(3, 'Bob Johnson', '[email protected]', 25, '2023-01-17');

Roadmap

Planned Features

• Database-specific SQL dialects (MySQL, PostgreSQL, SQLite, SQL Server)
• Advanced type inference with custom type mapping
• Batch file processing for multiple files
• SQL query builder for SELECT statements
• Data preview table before SQL generation
• Import from other different formats (JSON, XML)
• Schema validation and constraint generation
• Dark mode theme support

Known Issues

• Large files (>50MB) may cause performance issues
• Date format detection limited to common formats
• No support for nested JSON structures in CSV

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with React
• CSV parsing powered by Papa Parse
• Excel support via SheetJS

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Made with ❤️