Convert Excel or CSV to SQL: MySQL, Postgres, SQLite and SQL Server
Loading spreadsheet data into a database usually means turning rows into SQL INSERT statements, and doing that by hand is slow and error-prone. This guide walks through how Excel or CSV data maps to SQL tables, how to write the conversion yourself, and the syntax quirks that differ between MySQL, PostgreSQL, SQLite, and SQL Server.
Why Convert Excel or CSV to SQL?
Databases don't accept spreadsheets directly. Before you can query or join your data, it needs to exist as rows inside a table, which means translating each spreadsheet row into a SQL INSERT statement (or a bulk load file). This is a common step when migrating a client list from Excel into a MySQL app, importing a CSV export into PostgreSQL for analysis, or seeding a local SQLite database for testing.
How Spreadsheet Data Maps to SQL
Each column in your spreadsheet becomes a column in a table, and each row becomes one record. Before converting, check these three things:
- Header row: the first row should contain clean column names (no spaces or special characters) — these become your SQL column names.
- Data types: decide whether each column is text, a number, a date, or a boolean. Mixed types in one column (like "N/A" in a numeric field) will cause import errors.
- Table name: you'll need an existing table or a CREATE TABLE statement matching your columns and types.
Converting Manually, Step by Step
- Clean your spreadsheet: remove blank rows, fix inconsistent formatting, and standardize date formats (SQL generally expects YYYY-MM-DD).
- Write or generate a CREATE TABLE statement that matches your columns, e.g. CREATE TABLE customers (id INT, name VARCHAR(100), signup_date DATE);.
- For each row, build an INSERT statement: INSERT INTO customers (id, name, signup_date) VALUES (1, 'Jane Doe', '2024-03-15');.
- Wrap text values in quotes, leave numbers unquoted, and escape any apostrophes inside text (e.g. O'Brien becomes O''Brien).
- Run the resulting script against your target database using a client like MySQL Workbench, psql, or the SQLite CLI.
For a handful of rows this is manageable with formulas (Excel's CONCATENATE or TEXTJOIN can build INSERT strings), but it quickly becomes tedious and error-prone past a few dozen rows, especially with text that needs escaping.
Syntax Differences Between Database Engines
The basic INSERT syntax is similar across engines, but a few details differ:
| Database | Quoting identifiers | Notes |
|---|---|---|
| MySQL / MariaDB | Backticks: `table_name` | Flexible date parsing, supports AUTO_INCREMENT |
| PostgreSQL | Double quotes: "table_name" | Case-sensitive if quoted; strict type checking |
| SQLite | Double quotes or none | Loosely typed — most values can be stored as text |
| SQL Server | Square brackets: [table_name] | Uses GO batch separators; different date literal rules |
Common Use Cases
- Migrating a contact list from Excel into a MySQL-backed CRM.
- Loading a CSV export from an analytics tool into PostgreSQL for deeper querying with joins.
- Seeding a local SQLite database for app development or testing without a server.
- Populating a SQL Server table for a reporting dashboard built on spreadsheet data from finance or sales teams.
Convert Automatically
Instead of writing INSERT statements by hand or debugging quote-escaping issues, you can convert a file directly: our Excel to SQL converter or CSV to SQL converter detects your columns and generates ready-to-run SQL for MySQL, MariaDB, PostgreSQL, SQLite, or SQL Server in seconds.