CSV to SQL Converter
Convert CSV data to SQL CREATE TABLE and INSERT statements for multiple databases
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CSV to SQL Converter Documentation
What is this tool?
This tool converts CSV (Comma-Separated Values) data into SQL statements, including CREATE TABLE (DDL) and INSERT statements. It automatically infers column data types from CSV data and generates database-specific SQL syntax for multiple database systems.
Key Features
- Support for multiple database systems (MySQL, PostgreSQL, SQL Server, SQLite, Oracle)
- Automatic data type inference from CSV values
- Customizable table name and delimiter
- Optional DROP TABLE statement generation
- Column name detection from CSV header row
Common Use Cases
- Migrating data from spreadsheets to databases
- Creating database tables from exported CSV files
- Generating test data INSERT statements
- Converting legacy data to SQL format
Supported Databases
- MySQL - Popular open-source relational database
- PostgreSQL - Advanced open-source database with enterprise features
- SQL Server - Microsoft's enterprise-grade database solution
- SQLite - Lightweight embedded database
- Oracle - Enterprise commercial database
Best Practices
Check Generated Data Types
Auto-detected types are best guesses. Review and adjust VARCHAR lengths, use appropriate numeric types (INT vs BIGINT), and consider using DECIMAL instead of FLOAT for financial data.
Manually Add Constraints
The generator creates basic tables. Add PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, and CHECK constraints based on your data requirements and relationships.
Add Indexes for Frequently Queried Columns
After creating the table, add indexes for columns used in WHERE clauses, JOINs, and ORDER BY. But avoid over-indexing as it slows down INSERT operations.
Test with Sample Data First
First generate SQL for a subset of your CSV. Verify that table structure and data types work correctly before processing the full dataset.
Handle NULL Values Appropriately
Empty CSV fields become NULL or empty strings depending on context. Define clear NULL handling in your table design and adjust the generated SQL accordingly.
Use Transactions for Large Inserts
Wrap bulk INSERT statements in transactions (BEGIN/COMMIT). This improves performance and allows rollback if any insert fails.
Frequently Asked Questions
How do I import large CSV files?
For very large files (subject to server or browser limits), consider using database command-line tools (e.g., MySQL's `LOAD DATA INFILE` or PostgreSQL's `COPY`). This tool is optimized for browser-side processing of small to medium files.
Can I use custom column names?
Yes. If your CSV contains a header row, the tool will automatically use those names. You can also manually edit the generated SQL CREATE TABLE statement before execution to modify column names or types.
How are date formats handled?
The tool attempts to recognize standard date formats (ISO 8601). If your dates are in custom formats, they may be treated as strings. For best results, format dates in your CSV as YYYY-MM-DD.
Is my data secure?
Yes, this is a client-side tool. Your CSV data is processed entirely in the browser's JavaScript engine and is never uploaded to our servers.
Why are some numbers quoted?
If a column contains a mix of numbers and text, or numbers with currency symbols, the tool treats that column as VARCHAR (string) and quotes the values to prevent SQL errors.