CSV to SQL

The CSV to SQL Converter generates SQL INSERT statements from CSV data. Paste CSV, pick SQL dialect (MySQL, PostgreSQL, SQLite, SQL Server), specify table name, and get ready-to-run SQL. Handles data type auto-detection, proper escaping for strings, NULL values, and can produce single-row or multi-row (batch) INSERT statements for efficiency.

Useful for database seeding, data migration, importing spreadsheet data, or any scenario where you have CSV data and need to put it in a database. Saves time vs manual INSERT writing or complex ETL setup for one-off loads.

CSV to SQL conversion steps:

1. Parse CSV (handling quoting, escaping, encoding)
2. Detect column types (number, string, date, boolean, null)
3. Generate CREATE TABLE statement (optional)
4. Generate INSERT statements for each row

SQL dialects differ in:
- String quoting: single quotes standard
- Escaping: \\\"It\\\\\\\"s\\\" vs \\\"It\\\\'s\\\"
- Identifier quoting: backticks MySQL, double quotes PostgreSQL/SQLite
- Date format: YYYY-MM-DD standard, but some dialects strict
- Boolean: TRUE/FALSE vs 1/0
- NULL: NULL keyword consistent
- Auto-increment: AUTO_INCREMENT (MySQL), SERIAL (PostgreSQL), AUTOINCREMENT (SQLite)

Type detection:
- Pure digits: INTEGER
- With decimal: DECIMAL or FLOAT
- \\\"true\\\"/\\\"false\\\" (case-insensitive): BOOLEAN
- ISO date string: DATE
- ISO datetime: TIMESTAMP/DATETIME
- Otherwise: VARCHAR with max length from data

Example CSV:
id,name,age,active,created
1,Alice,30,true,2024-01-15
2,Bob,,false,2024-02-20

Generated INSERT (MySQL, multi-row):
INSERT INTO users (id, name, age, active, created) VALUES
(1, 'Alice', 30, TRUE, '2024-01-15'),
(2, 'Bob', NULL, FALSE, '2024-02-20');

Options:
- Single-row INSERT per row (more debuggable, slower)
- Multi-row INSERT (efficient for bulk load)
- Transaction wrapping (BEGIN/COMMIT)
- IGNORE or ON CONFLICT clauses
- Prepared statement format with placeholders

Escaping:
- Single quotes in strings: doubled ('it''s') in MySQL/Postgres/SQLite
- Backslash escape: '\\\\' in some MySQL configs
- Choice of escape strategy per dialect

Very large datasets:
- Multi-row INSERT faster than single-row
- Batch into chunks (1000-10000 rows per INSERT)
- Include LOCK TABLES for MySQL acceleration
- Disable autocommit for PostgreSQL speed

Data types in SQL vary:
- MySQL: INT, BIGINT, VARCHAR, TEXT, DATE, DATETIME, BOOLEAN
- PostgreSQL: INTEGER, BIGINT, VARCHAR, TEXT, DATE, TIMESTAMP, BOOLEAN
- SQLite: flexible typing, INTEGER, TEXT, REAL, BLOB
- SQL Server: INT, BIGINT, VARCHAR, NVARCHAR, DATE, DATETIME2, BIT

Tool generates appropriate types for chosen dialect.

Compared to manual SQL writing, this tool is far faster for many rows. Manual error-prone and tedious.

Compared to database-specific bulk load (LOAD DATA, COPY), this tool is simpler but slower for very large datasets. Use bulk commands for millions of rows.

Compared to ETL tools, this tool is for quick one-offs. ETL for complex pipelines and transformations.