⌘ K
csv → sql · bulk insert · numeric inference · live

Generate SQL INSERT from CSV

Turn a CSV file into INSERT statements for any SQL database. Numeric strings stay unquoted, blanks become NULL, and you can choose one bulk statement or one per row.

CSV → SQL INSERT
Input
100 B
4 lines
Output
175 B
5 lines
Status
ready
live
Ratio
1.75×
out/in
input · csv
output · sql
INSERT INTO `data` (`id`, `name`, `age`, `email`)
VALUES
  (1, 'Alice', 30, 'alice@example.com'),
  (2, 'Bob', 25, 'bob@example.com'),
  (3, 'Carol', 45, 'carol@example.com');
Quick Start

How CSV becomes SQL

RFC 4180 parsing splits the header row from data rows. Each value is checked for an integer or decimal shape; matching values stay unquoted, everything else is single-quoted with embedded quotes doubled.

  1. 01

    Set table name

    Backticks are added automatically — switch to double quotes for ANSI databases.

    Parsing is strict-but-tolerant — comments and trailing commas are accepted on input where the source format historically permits them; output stays canonical.

    tablestricttolerantliveparse
    tbl · sourcename · live
    data
    loaded · parsedtarget · ready
  2. 02

    Paste CSV

    First row is the column list; remaining rows are values.

    Conversion preserves type information where the target supports it (numbers stay numeric, booleans stay boolean) so the destination parser never has to re-coerce.

    inputtype-preservinglosslesslivememoised
    in · optionscsv · live
    id,name
1,Alice
    options applied · liveround-trip
  3. 03

    Pick a shape

    Bulk INSERT emits one statement with comma-separated tuples; toggle off for one INSERT per row.

    Re-encoding is idempotent: running output back through the same converter produces the same bytes, which keeps round-trips git-friendly.

    shapeidempotentround-tripdiff-cleandeterministic
    bulk · convertedoff? · ready
    INSERT INTO ...
VALUES (...), (...)
    converted · livediff-clean
  4. 04

    Copy or download

    Drop into a migration file, a seed script or paste straight into psql / mysql.

    Copy writes via navigator.clipboard.writeText; downloads use the target’s native MIME so editors apply the right syntax highlighting on open.

    exportclipboardtarget MIMEblobclient-side
    ✓ · copiedready · done
    ✓ Copied
    shipped · ready to shipsuccess
Pro tips

Converting between formats, safely

Pick the strictest source format

Lossy formats hide bugs. Choose JSON or YAML as your source-of-truth, convert downstream as needed.

Round-trip in CI

If your converter is idempotent, your CI can verify A → B → A equals the original. Catches subtle data loss.

Numbers vs strings differ

YAML/TOML have a number type; CSV doesn’t. Quote IDs in CSV to keep leading-zero values intact.

Watch character encoding

Source format may be Latin-1 / Windows-1252; target should be UTF-8. Convert encoding before structure.

Preserve comments where you can

YAML and TOML support comments. Don’t silently drop them — they often encode intent.

Schema-aware beats blind

Use a schema to guide conversion. Field types, required-ness, and defaults survive better with structure.

Related toolsJSON to SQLSQL to JSONCSV to YAMLCSV to XMLCSV to JSONCSV to TSV
Use Cases

When to import CSV as SQL

Seeds, migrations, one-off loads.

Database seeds

Generate seed scripts for development environments. Type information survives: numbers stay numeric, booleans stay boolean, strings stay strings. The destination parser doesn't have to re-coerce, which avoids the "'true' becomes "true"" bug class.

round-trip safe

Spreadsheet import

Move an Excel export straight into a relational table. Run output back through the inverse converter to verify idempotent round-trips in CI. A → B → A should produce byte-identical input — if it doesn't, you've found data loss.

type-preserving

Migration backfills

Backfill a new column from a CSV produced by another team. Comments and metadata survive where the target format supports them (YAML, TOML, HCL). The tool doesn't silently drop intent-encoded annotations, which keeps your config self-documenting.

schema-aware

Snapshot fixtures

Freeze production-like data as SQL fixtures for tests. Pair with a schema (JSON Schema, OpenAPI, Zod, Pydantic) to catch shape drift on either side. Catch breaking changes at conversion time rather than at runtime in production.

lossless

Ad-hoc reports

Re-import an exported report into a staging DB for SQL analysis. Output respects target-format conventions: quoting in CSV, indent in YAML, brackets in JSON. Existing parsers don't need patches, and the result drops into legacy pipelines unchanged.

diff-clean

Bulk uploads

Convert CSVs from third parties into reproducible SQL. Encoding-aware: handles UTF-8 vs UTF-16 vs Latin-1 / Windows-1252 transparently. The character-set surprise that breaks "works on my machine" doesn't apply here.

spec-grade
FAQ

Frequently asked questions

Integers and decimals (using a dot) stay unquoted. Everything else is treated as text and single-quoted with embedded quotes doubled per SQL standard.
Empty cells and the literal text NULL emit as unquoted NULL.
Quotes are doubled but the output is meant for one-off loads, not user input. Review before running against production.
Bulk INSERT is faster for large loads; single INSERTs are easier to skim during code review or partial replay.
No. CSV parsing and SQL generation are both local to your browser.
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