Online CSV data conversion tool: convert CSV to SQL INSERT statements, Markdown tables, or HTML tables with one click. Supports custom table names.
TL;DR: Free CSV SQL query tool powered by SQL.js (SQLite WASM). Supports SELECT, JOIN, and aggregate functions. All data stays in your browser, no registration.
For local tools, open DevTools → Network and search your input — it should not appear in requests.
Next steps
Usage Guide
Turn CSV into SQL INSERT / Markdown / HTML tables locally.
1. Convert
Paste CSV, set table name and identifier style, smoke-test ten rows.
2. Data hygiene
Fix quoting, nulls, and date/column type alignment first.
3. Identifiers
MySQL backticks vs Postgres quotes — pick for the target engine.
4. Fit
One-off drafts yes; million-row ETL no.
5. Safety
Local conversion still needs careful execution against production.
6. Related
Excel→CSV or TSV conversion may come first.
FAQ
Three outputs: SQL INSERT statements (with a custom table name), Markdown tables, and HTML tables. It fits small-to-medium one-off migrations and pasting tables into docs or READMEs — not a replacement for a real ETL pipeline.
MySQL wraps table and column names in backticks, Postgres in double quotes — critical when a column name collides with a reserved word like order. Picking the wrong style makes the statement fail outright on the target database; the tool offers a style switch but does not auto-detect the remote dialect.
Confirm the delimiter and whether the first row is a header. Commas, newlines, and double quotes inside fields must be properly quoted or the columns will misalign; nulls, numbers, and strings are written differently in SQL, and date/timezone formats must match the target column types. Spot-check a ten-row sample before converting the full file.
Treat them as a draft. Execute against a test database first, double-check the connection string and database name so nothing lands in production by accident, and prefer a trial run inside a transaction with a rollback ready — dirty data entering the database costs more than a failed conversion.
If the destination is analysis rather than a database, converting to JSON/Excel may fit better; you can also stage through Excel→CSV or CSV↔TSV first. Pick the path by what consumes the output next and skip pointless intermediate conversions.
No. The file content is parsed in local memory only, the converted output is produced right on the page, and nothing travels over the network.
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