Transform CSV data into SQL INSERT statements instantly. Perfect for importing spreadsheet data, migrating legacy systems, or creating database seeds.
Click "Convert" to generate SQL
Use this when the source system gives you CSV and you need runnable INSERT statements without writing a one-off script.
Useful when operations, finance, or marketing hands you a CSV and engineering needs reproducible rows for testing.
When header mode is on, the first row becomes SQL column names. If those names are messy, fix them before import so the generated schema stays readable.
CSV fields like "London, UK" remain a single value when quoted correctly. This matters for addresses, descriptions, and exported comments.
A mostly numeric column that includes values like N/A, -, or currency-formatted text will usually fall back to TEXT. Review the schema before you run it.
CSV (Comma Separated Values) is the universal language of data exchange. Whether it is from Excel, Google Sheets, or legacy systems, converting CSV files to SQL INSERT statements is necessary for importing data into relational databases. Our CSV to SQL Converter simplifies this task by parsing your files, auto-detecting column types, and generating optimized SQL queries ready for execution.
Manually writing SQL for thousands of spreadsheet rows is impractical. This tool handles the complexity of:
CREATE TABLE statements matching your data structure.INTEGER, REAL, BOOLEAN, or TEXT.DROP/CREATE statements..sql file for execution.Not all CSVs are comma-separated. European formats often use semicolons (;), and data often contains commas within the text itself (e.g., "Chicago, IL").
"1,000"). We respect the standard RFC 4180 rules."She said ""Hello""").Processing large datasets (10,000+ rows) requires efficient strategies. While our tool works great for medium-sized files (up to ~50MB) directly in the browser, here are tips for massive datasets:
VALUES clauses (batch size 100) to speed up database insertion.BEGIN TRANSACTION;
INSERT INTO users VALUES (...);
...
COMMIT;The converter looks at the values in each column to decide the best SQL type:
| CSV Value Example | Inferred SQL Type |
|---|---|
123, -42 | INTEGER |
12.34, 0.005 | REAL / DECIMAL |
true, false, 1, 0 | BOOLEAN (or TINYINT) |
2023-12-25 | TEXT (treated as ISO Strings) |
Hello World | TEXT / VARCHAR |
Migrate product catalogs, user lists, or financial data from Excel/CSV exports directly into your SQL database.
Design test scenarios in a spreadsheet, then convert them to SQL to seed your development or staging database.
Empty cells in the CSV are converted to SQL NULL values, assuming the column allows nulls. If it is a text column, it might be an empty string depending on your preference.
Currently, we auto-detect standard delimiters (comma, semicolon, tab, pipe). If your file uses a really obscure delimiter, try replacing it with a comma in a text editor first.
No problem. Standard CSV format handles commas within fields by enclosing the field in double quotes (e.g., "London, UK"). Our parser respects this standard.
Best follow-up when the imported CSV still needs trimming, casting, and deduplication after load.
Useful when inferred INTEGER, REAL, BOOLEAN, and TEXT types need to become a more deliberate production schema.
Helpful when CSV imports are becoming part of a repeatable seed-data workflow.
Useful when the CSV import is exposing a table shape that should be cleaned up before it becomes a long-term schema.
Use this path when the CSV is messy and conversion is only the first step before trimming values, deduplicating rows, or fixing null-heavy columns.
Relevant when the spreadsheet is exposing a table shape that should become a cleaner relational model rather than staying a one-off dump.
Choose this when the converted CSV is feeding test environments, demos, or longer-lived seed scripts instead of a single manual load.
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