CSV to SQL Converter

What Is This Tool?

Seeding a test database, loading a spreadsheet export into a table, or migrating a small dataset often starts the same way: you have a CSV file and you need SQL. This tool parses your CSV (using the header row as column names) and builds one INSERT INTO statement covering every data row, ready to paste into a database client.

String values are wrapped in single quotes with internal quotes escaped by doubling them (so O'Brien becomes O''Brien), which is the standard escaping rule shared by MySQL, PostgreSQL and SQLite. An empty CSV cell is written as the SQL keyword NULL rather than an empty string — that's a deliberate choice, since a blank cell in a spreadsheet usually means "no value" rather than "an empty piece of text", and NULL is what most schemas expect for that.

Whether a value gets quoted or left bare is decided by a simple heuristic, not real type inference: if the trimmed cell looks like a plain integer or decimal number (optionally with a leading minus sign), it's written unquoted; everything else — including things that merely resemble numbers with extra characters, like phone numbers or ZIP codes with leading zeros — is quoted as a string. This keeps the output honest: it's a good starting point for a small import, not a substitute for validating your data against your actual table schema before running it.

Source: CSV parsing follows RFC 4180, the closest thing to a formal CSV standard; single-quote doubling for string escaping is the shared convention documented by MySQL, PostgreSQL and SQLite.

Why Use It?

• Custom table name — set the target table once, and every row targets that table.
• Correct string escaping — single quotes inside values are doubled so the SQL doesn't break or get truncated.
• Predictable NULL handling — empty cells become NULL instead of an empty string, matching how most databases distinguish missing data.
• Common SQL syntax — the INSERT INTO ... VALUES ... form generated here works unmodified in MySQL, PostgreSQL and SQLite.
• 100% client-side — your CSV data (which may include customer records or business data) is parsed and converted in your browser and never uploaded anywhere.

How to Use

1. Paste CSV data with a header row, e.g. name,age\nAlice,30\nBob,25.
2. Enter the target table name (defaults to my_table).
3. Click "Generate SQL".
4. Copy the INSERT INTO statement or download it as a .sql file, then run it against your database.

Example

Input

name,age,city
Alice,30,
Bob,,NYC

Output

INSERT INTO `my_table` (`name`, `age`, `city`)
VALUES
  ('Alice', 30, NULL),
  ('Bob', NULL, 'NYC');

Notice the empty cells became NULL (not empty strings), and the numeric-looking age value 30 was left unquoted while every text value was quoted.

Practical tips

• Seeding a local test database: export a small sample as CSV from a spreadsheet, convert it here, and run the INSERT statement against your dev database.
• Always review column types before running the generated SQL against a real table — this tool infers numeric vs. text with a simple heuristic, not your actual schema, so an integer column expecting a specific format may need manual adjustment.
• If you have thousands of rows, the single multi-row INSERT statement this tool generates is still valid SQL, but very large statements can hit a database's max packet size — split the CSV into smaller batches first if you run into that limit.

Why NULL and not an empty string

A common mistake when hand-writing CSV-to-SQL scripts is treating every missing cell as an empty string ''. That's technically valid SQL, but it usually doesn't mean what you want: a column defined as an integer will reject '' outright, and even in a text column, '' silently means something different from "we don't know this value" in most schema designs and reporting queries (a WHERE column IS NULL check won't match empty strings, and vice versa). Using NULL for blank cells matches the semantics most databases and ORMs expect, and it avoids type errors when a numeric column happens to have missing values in some rows.

Frequently Asked Questions