CSV to SQL Converter

About the CSV to SQL Converter

This tool turns CSV spreadsheets into SQL INSERT statements and a matching CREATE TABLE definition, so you can import data into relational databases without manual scripting. It’s ideal for migrations, quick prototypes, or generating fixture data for SQL engines.

Key Features

• CREATE TABLE Generation: Builds a CREATE TABLE csv_data (...) statement using sanitized column names.
• INSERT Statements: Produces individual INSERT INTO commands for every row.
• Delimiter Support: Works with comma, semicolon, tab, and pipe-delimited CSV files.
• Quote Escaping: Handles embedded quotes by doubling them (O'Reilly → 'O''Reilly').
• Header Awareness: Uses the first row as column names when available.
• Instant Preview & Download: Real-time SQL output with copy/download buttons.

How to Convert CSV to SQL

1. Paste or Upload CSV: Provide the CSV data in the input panel or upload a .csv file.
2. Configure Parsing: Select the correct delimiter and toggle “First row is header.”
3. Review SQL Output: Inspect the generated CREATE TABLE and INSERT statements.
4. Copy or Download: Copy the SQL to your clipboard or download it as output.sql.
5. Run in Your Database: Execute the script in MySQL, PostgreSQL, SQL Server, SQLite, or any ANSI SQL-compatible engine.

Output Structure

• Table Name: Defaults to csv_data (rename as needed before running).
• Column Types: Uses VARCHAR(255) for simplicity—adjust to more specific types if required.
• Insert Mode: Generates one INSERT per row for clarity and easy diffing.
• Error Comments: If no headers exist, the output explains why CREATE TABLE was skipped.

Common Use Cases

• Database Seeding: Load small datasets into dev/test databases quickly.
• Data Migration: Convert legacy CSV exports into SQL scripts for import.
• ETL Prototyping: Validate column mappings before building full pipelines.
• Documentation: Provide SQL snippets that recreate sample tables in tutorials.
• Bug Reproduction: Share a minimal SQL script that reproduces an issue.

Best Practices

• Review and adjust column types (e.g., INT, DATE, DECIMAL) to match production schemas.
• Rename csv_data to a descriptive table name before running in production.
• Wrap the generated SQL in a transaction (BEGIN; / COMMIT;) for easy rollback.
• For very large CSVs, consider batch imports or LOAD DATA commands for better performance.

FAQ

• Which SQL dialect does this use? The generated SQL is generic and should work in most ANSI SQL databases. Column types default to VARCHAR(255); adjust them for MySQL, PostgreSQL, SQL Server, or SQLite as needed.
• Can I customize the table name? Yes. The default table name is csv_data. Before running the script, simply replace that name with one that matches your schema.
• How are quotes and special characters handled? Single quotes in values are doubled (for example, O'Reilly becomes 'O''Reilly') so the INSERT statements remain valid.
• What happens if there is no header row? When no headers are present, the tool cannot safely build a CREATE TABLE statement and instead outputs a comment explaining that headers are required.
• Is this safe for production data? The conversion runs entirely in your browser. You should still review the generated SQL, test it in a non-production database first, and make backups before bulk imports.

Privacy & Security

All CSV to SQL conversions happen locally in your browser via client-side JavaScript. Your CSV files and generated SQL never leave your computer, ensuring sensitive data remains private.

Start Converting CSV to SQL

Paste your CSV, tweak delimiter settings, and instantly generate SQL INSERT statements plus a CREATE TABLE script. Perfect for quick imports, demos, or migrating CSV datasets into relational databases.