CSV to SQL Converter

Convert CSV spreadsheets into SQL CREATE TABLE schema definitions and batch INSERT INTO statements.

CSV Input & Configuration

Paste Raw CSV Content (First row as headers)

Target Table Name

SQL Dialect

Include CREATE TABLE DDL

Insert Format:

Generated SQL Statements

CREATE TABLE `employees` (
  `id` INT,
  `name` VARCHAR(255),
  `email` VARCHAR(255),
  `role` VARCHAR(255),
  `salary` DECIMAL(10,2),
  `is_active` BOOLEAN,
  `created_at` DATE
);

INSERT INTO `employees` (`id`, `name`, `email`, `role`, `salary`, `is_active`, `created_at`)
VALUES
  (1, 'Alice Johnson', 'alice@example.com', 'Engineer', 115000.5, TRUE, '2026-01-15'),
  (2, 'Bob Smith', 'bob@example.com', 'Manager', 128000, TRUE, '2026-02-01'),
  (3, 'Clara Vance', 'clara@example.com', 'Designer', 92000.75, FALSE, '2026-03-10'),
  (4, 'David Miller', 'david@example.com', 'Analyst', 85000, TRUE, '2026-04-05');

Auto-Inferred Schema TypesSafe Quoted & Escaped Strings

Seamless Spreadsheet to Relational Database Conversion

Comma-Separated Values (CSV) are the universal lingua franca for exporting data from spreadsheets like Excel and Google Sheets. Converting CSV data into SQL statements allows software developers, database administrators, and data engineers to populate relational database schemas without writing custom ETL parsing scripts.

Dialect Compatibility

MySQL / MariaDB: Uses backtick identifiers and standard ANSI string escaping.
PostgreSQL: Uses double-quote table and column identifiers with standard boolean literals.
SQLite: Produces compact DDL and multi-row value insertions compatible with SQLite 3.7.11+.

Developer & DBA Scenarios

✓
Database Seeding & Test Fixture Generation
Quickly transform exported spreadsheet mock data into executable SQL migration seeds for local development environments.
✓
Legacy Data Migration into MySQL or PostgreSQL
Import customer records, financial ledgers, or product inventories from Excel exports into production relational databases.
✓
SQLite Embedded Database Setup
Generate lightweight table creation and insert scripts for mobile and desktop applications powered by SQLite.
✓
Analytics Data Warehouse Staging
Format flat CSV log files into structured SQL insert statements for BigQuery, Snowflake, or Amazon Redshift staging tables.

Why Use This CSV to SQL Converter

✓

Automatic Data Type Inference

Scans your data rows to detect INTEGER, DECIMAL, BOOLEAN, DATE, and VARCHAR data types automatically.

✓

Multi-Dialect SQL Syntax Support

Generates tailored syntax with appropriate quote identifiers for MySQL (backticks), PostgreSQL (double quotes), SQLite, and MS SQL Server.

✓

Batch vs Single INSERT Formatting

Choose high-performance multi-row `INSERT INTO ... VALUES (), ()` batch statements or individual transactional statements.

✓

One-Click `.sql` File Export

Copy generated queries directly to your clipboard or download a structured `.sql` file ready for command-line database imports.

Frequently Asked Questions

How does the CSV to SQL converter determine column types?

The converter inspects sample data rows: values containing only digits are assigned INT, values with decimals are assigned DECIMAL, 'true'/'false' are assigned BOOLEAN, 'YYYY-MM-DD' dates are assigned DATE, and text strings default to VARCHAR(255).

Are quotation marks and special characters escaped safely?

Yes! Single quotes within string values are escaped to double single quotes (`''`) in compliance with ANSI SQL standards, preventing syntax errors and injection vulnerabilities during script execution.

What is the difference between batch insert and single insert?

A batch insert bundles multiple records into a single `INSERT INTO table (columns) VALUES (...), (...)` statement, which is dramatically faster when loading hundreds of rows. Single inserts execute a separate statement for each row.

Is there any row limit on client-side conversion?

Because conversion is executed in your browser's local memory, you can convert thousands of rows without hitting server timeout limits or compromising sensitive corporate data.