Have data in a CSV that needs to go into a database? Paste or upload the CSV and get ready-made SQL INSERT statements you can run against your table. It saves writing each row out by hand when you are seeding or importing data.
Offline CSV to SQL Converter | Instant Database Scripts, Completely Private & Free
Convert CSV data to SQL INSERT queries instantly with our 100% offline, secure CSV to SQL Query Converter. Generate CREATE TABLE scripts locally in your browser.
Table of Contents
Options
CSV input
SQL output
SQL input (INSERT statements)
CSV output
CSV input
Table output
Paste CSV to validate and preview
6 Dialects, 6 Statements
Generate INSERT, CREATE TABLE, UPDATE, DELETE, and UPSERT for MySQL, PostgreSQL, SQL Server, SQLite, Oracle, or Standard SQL — each with the right identifier quoting and syntax for that database.
Smart Type Inference
It reads each column and picks a sensible type — integer, decimal, date, boolean, or a sized VARCHAR — and flags which columns can be null, so your CREATE TABLE is ready to run, not just a wall of text fields.
Two-Way & Table Export
Go the other way and turn INSERT statements back into CSV, or export any CSV as a clean Markdown or HTML table. A preview tab validates your columns before you convert.
How to Use the CSV to SQL Converter
Paste your CSV
Drop your spreadsheet data in, or press Sample to see the expected format.
Set the options
Pick your table name, dialect, and statement type, and toggle type inference or cleaning.
Convert
Press Convert to SQL and read the generated queries in the output panel.
Copy or download
Copy the SQL or download a .sql file, ready to run in your database.
Last updated: August 2026
🔴 What it converts, and when you’d reach for it
Getting data out of a spreadsheet and into a database is a chore every developer meets sooner or later. You export a CSV, and now you need it as rows in a table. This csv to sql converter does that in one step: paste the CSV, and it writes the INSERT statements, or a full CREATE TABLE, for the database you actually use. It runs entirely in your browser, so a customer list or a private dataset never gets uploaded to a server to be processed.
People reach for it to seed a new table from sample data, migrate a small dataset between systems, generate test fixtures, or turn a client’s spreadsheet into a real schema. Because it handles six dialects, the output is not generic SQL you have to hand-fix — it matches MySQL, PostgreSQL, SQL Server, SQLite, Oracle, or the Standard.
🟡 From spreadsheet to database in a few clicks
Start by pasting your CSV and choosing whether the first row is a header. Pick a table name and a dialect, then choose what you want: plain INSERTs, a CREATE TABLE, both together, or an UPDATE, DELETE, or UPSERT keyed on a column you select. The cleaning toggles trim stray spaces, turn empty cells into proper NULLs, and pack rows into a single multi-row INSERT for speed. The converter reads quoted fields correctly, so a value like a product name with a comma in it stays in one column instead of splitting.
The tool works both directions. Paste existing INSERT statements into the SQL-to-CSV tab and it pulls the rows back out as CSV. If what you actually want is a formatted table for documentation or a README, the CSV-to-Table tab produces a Markdown or HTML table instead. For converting the same data to or from JSON, that job belongs to JSON Studio Pro, and for cleaning messy source text first, the regex tester is handy.
🟢 Getting the data types and dialect right
The part that saves the most time is type inference. With it on, the tool scans each column and chooses a type: a column of whole numbers becomes an integer, one with decimals becomes a numeric type, dates and true/false columns are detected, and everything else becomes a VARCHAR sized to the longest value. It also marks a column nullable if any cell is empty. You can switch inference off to treat every value as text when you would rather be safe, and you can name a primary key so the CREATE TABLE is complete. Databases vary in how strictly they enforce these types — SQLite, for one, is famously flexible, as its datatype documentation explains.
One safety note worth understanding: the converter escapes single quotes in your values so the SQL stays valid, but generated INSERTs are meant for trusted data you are loading yourself, not for building queries from untrusted user input — for that you always use parameterised queries. Keeping this kind of work on your own machine is the same privacy logic covered in this guide on client-side data processing. If you want the full picture of how the conversion works under the hood, the companion article on how to convert CSV to SQL walks through it.
Is it free, offline, and private?
Yes. The conversion runs in your browser, so your data never leaves your device. There is no account, no upload, and it keeps working without a connection after the page loads.
Which SQL dialects are supported?
Standard SQL, MySQL, PostgreSQL, SQL Server, SQLite, and Oracle. Each gets the correct identifier quoting and data types, so the output runs without hand-editing.
What statement types can it generate?
INSERT, CREATE TABLE, both together, UPDATE, DELETE, and UPSERT. For UPDATE, DELETE, and UPSERT you pick the key column the statements match on.
What does type inference do, and can I turn it off?
It reads each column and picks a suitable SQL type instead of making everything text. You can switch it off to treat every value as a string when you prefer full control.
Does it handle commas inside quoted fields?
Yes. It follows standard CSV quoting rules, so a quoted value containing a comma or a line break stays in a single column rather than splitting incorrectly.
Can it convert SQL back to CSV?
Yes. The SQL-to-CSV tab parses INSERT statements and pulls the rows back out as CSV, quoting any values that contain the delimiter.
Can I get a Markdown or HTML table instead?
Yes. The CSV-to-Table tab turns your data into a Markdown table for docs or an HTML table for a web page, ready to copy or download.
Is the generated SQL safe from injection?
It escapes quotes so your own data loads cleanly. For queries built from untrusted user input, always use parameterised statements rather than generated string SQL.
Does it work on large files or a phone?
It handles typical datasets comfortably and stacks into a single column on phones. Very large files are better split, since everything is processed in memory.
