{ CSV to SQL }

CSV to SQL Converter — Import CSV Data into Any Database

CSV is the universal format for tabular data export — every spreadsheet application, SaaS platform, and database tool can produce a CSV file. But getting that data into a relational database requires SQL, specifically INSERT INTO statements with properly quoted values, escaped strings, and correct NULL handling. Writing those statements by hand for hundreds or thousands of rows is error-prone and slow. This tool generates them instantly from any CSV input.

MySQL, PostgreSQL, and SQLite Support

Each SQL dialect has different conventions for identifier quoting and upsert syntax. MySQL wraps column and table names in backticks — `table_name` — to avoid conflicts with reserved words. PostgreSQL uses double-quoted identifiers — "table_name". SQLite accepts both but conventionally uses backticks. The UPSERT statement differs across all three: MySQL uses ON DUPLICATE KEY UPDATE, PostgreSQL uses ON CONFLICT DO NOTHING, and SQLite uses INSERT OR REPLACE INTO. Selecting the right dialect from the dropdown generates output that runs without modification in your target database.

CREATE TABLE and INSERT Together

When migrating data into a new table, you need both the schema definition and the data rows. The CREATE + INSERT mode generates a CREATE TABLE IF NOT EXISTS statement with all columns typed as TEXT, followed by the full set of INSERT statements. Copy the output, run it against your database, and the table is created and populated in a single operation. Edit the column types in the CREATE TABLE block to match your intended schema — change TEXT to INT, DECIMAL, DATE, or whatever your data requires.

Spreadsheet Migration and Data Seeding

Two of the most common CSV-to-SQL workflows are spreadsheet migration and database seeding. Migrating a Google Sheets or Excel workbook into a MySQL or PostgreSQL database means exporting each sheet as CSV, converting it to INSERT statements, and running the import — a process that can be completed in minutes with this tool. Database seeding for development environments follows the same pattern: export test data as CSV, generate INSERT statements, and include them in migration scripts or fixture files for repeatable environment setup.

NULL and Numeric Detection

Correct NULL and type handling is critical for data integrity. This converter detects empty CSV cells and the literal string NULL (case-insensitive) and outputs SQL NULL rather than an empty string or the word "NULL" as a string literal. Integer and decimal values are output unquoted so they are stored as numbers rather than strings. This means numeric columns will sort, aggregate, and index correctly without requiring a post-import type conversion step.