What is CSV to SQL Converter?
Moving data from spreadsheets or CSV exports into a relational database typically requires writing CREATE TABLE statements with the right column types and INSERT statements with properly escaped values. This tool automates that entire process: it reads your CSV, inspects every value in each column to infer the most specific SQL type, and generates both the DDL (CREATE TABLE) and DML (INSERT) statements in one pass. It supports three major SQL dialects — PostgreSQL, MySQL, and SQLite — and adjusts syntax details like auto-increment keywords, boolean representations, and date/time type names accordingly. Column names are sanitized to valid SQL identifiers, empty values become NULL, and the id column is automatically promoted to a primary key with the correct auto-increment syntax for the chosen dialect.
How to Use
- Enter a table name in the options bar — this becomes the name used in the CREATE TABLE and INSERT statements.
- Select your target SQL dialect (PostgreSQL, MySQL, or SQLite) so the output uses the correct syntax and type names.
- Paste your CSV data into the input area — the first row must contain column headers.
- Click 'Generate SQL' to produce the CREATE TABLE statement followed by INSERT statements for every data row.
- Copy the output into your database client or save it as a .sql file for later execution.
Why Use This Tool?
Tips & Best Practices
- The first row must be headers — the tool uses them as column names and converts them to valid SQL identifiers (e.g., First Name becomes first_name).
- Boolean values are handled per dialect: PostgreSQL and MySQL use TRUE/FALSE, while SQLite uses 1/0 since it lacks a native BOOLEAN type.
- If your table already exists, copy only the INSERT statements from the output and skip the CREATE TABLE portion.
- VARCHAR lengths are calculated from the longest value in each column, doubled for headroom, and capped at 255 — values exceeding that become TEXT.
- For large datasets, consider generating the SQL in batches to avoid hitting query size limits in your database client.
Frequently Asked Questions
How are CSV column types mapped to SQL types?
The converter inspects every value in each column: integers map to INTEGER, decimals to DECIMAL(10,2), true/false to BOOLEAN (or INTEGER for SQLite), ISO date strings to DATE, ISO timestamps to TIMESTAMP or DATETIME depending on dialect, and everything else to VARCHAR(n) or TEXT. The most specific type that fits all values is chosen.
When should I NOT use this converter?
Avoid this tool when your CSV has no header row, when you need per-column type overrides (e.g., forcing a numeric-looking string to stay VARCHAR), or when you require advanced SQL features like indexes, constraints, foreign keys, or partitioning. The generated SQL is a starting point for simple table creation, not a replacement for a carefully designed schema.
What are the differences between the supported SQL dialects?
PostgreSQL uses SERIAL for auto-increment IDs and TIMESTAMP for date-time values. MySQL uses INT AUTO_INCREMENT and DATETIME. SQLite uses INTEGER PRIMARY KEY AUTOINCREMENT and stores dates as TEXT since it has no native date type. Boolean handling also differs: SQLite stores booleans as 1/0.
How are column names sanitized?
CSV header values are converted to valid SQL identifiers: spaces become underscores, special characters are removed, and everything is lowercased. For example, "First Name" becomes first_name and "E-mail Address" becomes email_address.
Can I use this with an existing table?
The generated CREATE TABLE statement is for new tables. If your table already exists, copy only the INSERT statements from the output. Make sure the column order and types in the generated INSERT statements match your existing table schema.
Is my CSV data sent to any server?
No. All parsing and SQL generation runs entirely in your browser. Your CSV data and the generated SQL never leave your device. No data is collected, stored, or transmitted to any server.
Real-world Examples
User Registration Data to PostgreSQL
Convert a CSV of user registrations into PostgreSQL CREATE TABLE and INSERT statements with proper type inference.
id,name,email,age,is_active,created_at 1,Alice Johnson,alice@example.com,28,true,2024-01-15 10:30:00 2,Bob Smith,bob@example.com,35,false,2024-02-20 14:15:00
CREATE TABLE users (
id SERIAL PRIMARY KEY,
name VARCHAR(30),
email VARCHAR(50),
age INTEGER,
is_active BOOLEAN,
created_at TIMESTAMP
);
INSERT INTO users (id, name, email, age, is_active, created_at)
VALUES (1, 'Alice Johnson', 'alice@example.com', 28, TRUE, '2024-01-15 10:30:00');
INSERT INTO users (id, name, email, age, is_active, created_at)
VALUES (2, 'Bob Smith', 'bob@example.com', 35, FALSE, '2024-02-20 14:15:00');Product Catalog to SQLite
Convert a product catalog CSV into SQLite-compatible SQL with proper boolean and type handling.
id,product_name,price,in_stock 1,Wireless Mouse,29.99,true 2,Mechanical Keyboard,149.99,true 3,USB Hub,24.50,false
CREATE TABLE products (
id INTEGER PRIMARY KEY AUTOINCREMENT,
product_name VARCHAR(50),
price DECIMAL(10,2),
in_stock INTEGER
);
INSERT INTO products (id, product_name, price, in_stock)
VALUES (1, 'Wireless Mouse', 29.99, 1);
INSERT INTO products (id, product_name, price, in_stock)
VALUES (2, 'Mechanical Keyboard', 149.99, 1);
INSERT INTO products (id, product_name, price, in_stock)
VALUES (3, 'USB Hub', 24.50, 0);