CSV to SQL Converter
Generate SQL INSERT statements from CSV data. Customize table name and automatic type detection.
Safe conversion with no data sent to server
Last updated: March 2026
What is CSV to SQL Conversion?
CSV to SQL conversion is the process of transforming comma-separated data into SQL INSERT statements that can be executed against a relational database. This is one of the most common data import workflows in backend development: you receive data in a CSV file (from a spreadsheet export, a data vendor, or a manual data entry process) and need to load it into a SQL database like MySQL, PostgreSQL, SQLite, or SQL Server.
Rather than manually writing INSERT statements for each row, this tool automates the process. It reads the CSV header row to determine column names, inspects each value to detect data types (strings, numbers, NULLs), and generates properly formatted SQL statements. For instance, a CSV with columns "name,age,email" and a row "Alice,30,[email protected]" becomes: INSERT INTO table_name (name, age, email) VALUES ('Alice', 30, '[email protected]');
The tool also handles special characters by escaping single quotes in string values, preventing SQL injection vulnerabilities in the generated statements. Empty fields are converted to NULL values, maintaining database integrity.
How to Use This CSV to SQL Tool
Generate SQL INSERT statements from your CSV data in just a few steps:
- Upload a CSV file or paste your CSV data directly into the Input field.
- Enter your target table name in the "Table Name" field (defaults to "table_name"). This will be used in the generated INSERT INTO statements.
- Click "Convert to SQL" to generate the INSERT statements.
- Review the output SQL. Each CSV row becomes a separate INSERT statement with properly typed values.
- Copy the SQL to your clipboard or download it as a .sql file to execute in your database client.
Important notes: The first row of your CSV must contain column headers, as these become the column names in the SQL statements. Numeric values are detected automatically and inserted without quotes, while text values are properly quoted and escaped. Empty cells are represented as NULL.
Common Use Cases
- Database seeding: Populate development or staging databases with test data exported from spreadsheets.
- Data migration: Move data from CSV-based systems or Excel files into relational databases during system upgrades.
- Bulk data import: Load large datasets from CSV exports into MySQL, PostgreSQL, or SQLite tables.
- Quick prototyping: Generate INSERT statements for rapid database schema testing without writing SQL by hand.
- ETL pipelines: Use as an intermediate step to transform flat CSV files into SQL as part of an Extract-Transform-Load workflow.
- Legacy system integration: Bridge the gap between older systems that export CSV reports and modern SQL databases.
FAQ
Does the tool handle special characters in my data?
Yes. Single quotes within string values are escaped (doubled) to produce valid SQL. For example, the value "O'Brien" becomes 'O''Brien' in the generated SQL, preventing syntax errors when executing the statements.
Which SQL databases are compatible with the output?
The generated INSERT statements use standard ANSI SQL syntax, which is compatible with MySQL, PostgreSQL, SQLite, SQL Server, MariaDB, and most other relational databases. Minor syntax differences between databases (such as quoting conventions) may require small adjustments for some edge cases.
How are empty CSV fields handled?
Empty fields in the CSV are converted to SQL NULL values rather than empty strings. This is the most common and database-friendly approach, as it distinguishes between "no value" (NULL) and "an empty text value" ('').
Does this tool create the CREATE TABLE statement too?
Currently, the tool generates only INSERT statements. You will need to create the target table manually or use a separate tool to generate the CREATE TABLE DDL. The column names from the CSV headers can guide you in defining the table schema.