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  3. CSV to SQL
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CSV to SQL

Generate SQL INSERT and CREATE TABLE statements

Turn a CSV file into ready-to-run SQL. The tool generates CREATE TABLE statements with appropriate column types and INSERT statements for every row, compatible with MySQL, PostgreSQL, SQLite, SQL Server, and Oracle. Just upload, configure, and paste the output into your database client.

Rows never leave your deviceMore csv & data analysisJump to full guide

Related reading

  • SQL Formatting and Best Practices: Writing Readable, Maintainable Queries13 min read

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A sample run

You need to load a CSV into a database and want a ready CREATE TABLE plus INSERT statements with sane types.

CSV

id,name,active
1,Dana,true
2,Sam,false
CSV to SQL produces

SQL

CREATE TABLE data (
  id INTEGER, name TEXT, active BOOLEAN
);
INSERT INTO data VALUES (1,'Dana',true),(2,'Sam',false);

Types are inferred from the data and string values are properly escaped, so the generated SQL runs without the quoting and type errors hand-conversion introduces. You can choose the dialect and batch size for large files.

What is CSV to SQL?

Turn a CSV file into ready-to-run SQL. The tool generates CREATE TABLE statements with appropriate column types and INSERT statements for every row, compatible with MySQL, PostgreSQL, SQLite, SQL Server, and Oracle. Just upload, configure, and paste the output into your database client.

How to use

  1. 1Upload your CSV file
  2. 2Select the target SQL dialect
  3. 3Review and adjust auto-detected column types
  4. 4Set table name and primary key if needed
  5. 5Copy the generated SQL or download as a .sql file

Key features

  • MySQL, PostgreSQL, SQLite, SQL Server, and Oracle support
  • Auto column type detection (INT, DECIMAL, VARCHAR, DATE, BOOLEAN)
  • Configurable VARCHAR lengths based on data
  • Batch INSERT statements for performance
  • Primary key and NOT NULL constraint options
  • Output modes: CREATE only, CREATE + INSERT, INSERT, UPDATE, or UPSERT
  • Proper identifier quoting per dialect

How it works

Importing CSV data into a relational database usually means writing a CREATE TABLE statement by hand, guessing column types, and then either using COPY/LOAD DATA or writing INSERT statements. This tool automates the entire process. It detects column types (integer, decimal, varchar, date, boolean), picks sensible lengths for varchar columns based on actual data, and generates standards-compliant SQL. Besides plain INSERT, it can emit CREATE TABLE only, CREATE plus INSERT, UPDATE, or UPSERT statements.

Dialect differences are handled per target: MySQL gets backtick-quoted identifiers and AUTO_INCREMENT, PostgreSQL gets double-quoted identifiers and SERIAL, SQLite gets simplified TEXT/INTEGER types and AUTOINCREMENT, SQL Server gets square-bracket identifiers and IDENTITY, and Oracle gets GENERATED ALWAYS AS IDENTITY. Batch INSERT mode groups rows into multi-value inserts for faster execution on the dialects that support it.

Where this fits a data pipeline

  • Quick database seeding

    Generate INSERT statements from a CSV to populate a development or staging database.

  • Schema generation

    Let the tool draft a CREATE TABLE statement based on real data, then refine it.

  • Data migration scripts

    Convert exported CSV data into SQL scripts that can be version-controlled and replayed.

  • Learning SQL

    See how your data would be structured in SQL to understand table design and data types.

Frequently asked questions

Which SQL dialects are supported?

MySQL, PostgreSQL, SQLite, SQL Server, and Oracle. Each dialect uses its own quoting, type names, and auto-increment syntax.

Can I customize the column types?

Yes. The tool auto-detects types but you can override any column to a different type before generating SQL.

How does it handle special characters in data?

Values are properly escaped for the selected dialect, preventing SQL injection and syntax errors.

Is there a row limit?

No hard limit. For very large files, consider using your database's native COPY or LOAD DATA command instead of INSERT statements for better performance.

Further reading

  • SQL Formatting and Best Practices: Writing Readable, Maintainable Queries13 min read

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Rows and columns are parsed and transformed in memory in your browser. No record ever reaches a server.