Why Convert Excel to SQL?
Converting Excel spreadsheets to SQL is essential for database migration, data seeding, and ETL (Extract, Transform, Load) pipelines. Whether you're populating a development database, migrating data to production, or sharing structured data with a development team, SQL INSERT statements are the standard way to load data into relational databases. This tool transforms your .xlsx or .xls files into ready‑to‑run SQL – perfect for PostgreSQL, MySQL, SQLite, and SQL Server.
How to Use the Excel to SQL Converter
- Upload – drag & drop your Excel file or click to browse.
- Configure – set the table name, choose your SQL dialect, batch size, and quote style.
- Options – toggle headers, column name quoting, DROP TABLE, and CREATE TABLE statements.
- Generate – click Generate SQL to produce the INSERT statements.
- Copy or Download – copy the SQL to your clipboard or download it as a
.sqlfile.
The tool automatically maps Excel column names to SQL column names, handles data types (strings, numbers, dates), and escapes special characters. Large datasets are batched into multiple INSERT statements for efficiency.
Privacy & Security
- 🔒 All processing happens in your browser – no files are uploaded.
- 🚫 No server interaction – everything runs locally using SheetJS.
- 🕵️ No tracking, no logging – your data stays yours.
- 💼 Safe for sensitive data – perfect for financial or customer spreadsheets.
Frequently Asked Questions
Which SQL dialects are supported?
MySQL, PostgreSQL, SQLite, and SQL Server. The tool adapts quoting, escaping, and syntax accordingly.
Does this tool support multiple sheets?
Only the first sheet is converted. You can select a different sheet from the dropdown after uploading.
How are data types handled?
Numbers become numeric literals, strings are quoted, and empty cells become NULL. Dates are converted to string literals in standard format.
What is batch size?
Batch size controls how many rows are included in each INSERT statement. Larger batches produce fewer statements and can run faster in some databases.