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CSV to SQL Converter

Convert CSV data into INSERT statements for PostgreSQL, MySQL, or SQLite. Optionally emits CREATE TABLE with inferred types.

About CSV to SQL Converter

CSV to SQL converts comma-separated (or tab/semicolon/pipe-separated) data into ready-to-run INSERT statements for PostgreSQL, MySQL, or SQLite. Paste or upload a CSV file, set the table name, pick the dialect, and the tool emits either a single bulk INSERT statement (faster, fewer round trips) or one INSERT per row (easier to filter or roll back individually). Optionally include a CREATE TABLE statement with column types inferred from the actual data — INTEGER for whole numbers, NUMERIC for decimals, BOOLEAN for true/false/0/1 columns, VARCHAR sized to fit the longest value, or TEXT for very long strings. Identifiers are quoted using the conventions of each dialect (backticks for MySQL, double quotes elsewhere) and string values are escaped to prevent SQL syntax errors. Everything runs in your browser, so business data and exports with PII never reach a remote server.

Why use CSV to SQL Converter

Three Major Dialects

PostgreSQL, MySQL, and SQLite are supported with correct identifier quoting and boolean literal syntax for each — no manual fix-ups before running the SQL.

Type Inference From Data

CREATE TABLE statements get accurate column types (INTEGER, NUMERIC, BOOLEAN, VARCHAR, TEXT) inferred from real values, so the schema fits the data on first import.

Bulk vs Single-Row Mode

Choose a single multi-row INSERT (faster, fewer transactions) or one statement per row (easier to debug, filter, or roll back individually) depending on your import workflow.

Smart Identifier Quoting

Table names and column headers are quoted with backticks for MySQL and double quotes for PostgreSQL/SQLite, preventing collisions with reserved words.

String Escaping

Single quotes inside CSV values are doubled (SQL standard escape), preventing syntax errors and SQL injection from malformed source data.

Browser-Only Privacy

CSV data — including business PII, customer records, and financial data — is parsed and converted entirely in your browser. Nothing is uploaded or logged.

How to use CSV to SQL Converter

  1. Paste your CSV data or click Upload CSV to load a file from disk.
  2. Set the table name (any identifier; non-alphanumerics are sanitised to underscores).
  3. Choose a delimiter — Auto-detect handles most cases, or pick comma, tab, semicolon, or pipe.
  4. Pick the SQL dialect: PostgreSQL, MySQL, or SQLite. Quoting and boolean syntax adapt automatically.
  5. Toggle Bulk INSERT for a single multi-row statement, or off for one INSERT per row, and toggle Include CREATE TABLE to add a typed schema definition.
  6. Click Generate SQL and copy or download the result — paste directly into psql, mysql, sqlite3, or your favourite database client.

When to use CSV to SQL Converter

  • Bulk-loading a CSV export from Excel into a fresh PostgreSQL table for analysis.
  • Generating seed data for a SQLite test database from a small CSV fixture.
  • Migrating tabular data from a spreadsheet into a MySQL production table for the first time.
  • Producing a database snapshot SQL script from a CSV backup for archival or version control.
  • Loading event logs from a CSV into a Postgres table for ad-hoc SQL analytics queries.
  • Generating an INSERT script from a CSV download for an admin tool that does not support direct upload.

Examples

PostgreSQL with CREATE TABLE

Input: id,name,age,active 1,Alice,30,true 2,Bob,28,false

Output: CREATE TABLE "my_table" ( "id" INTEGER, "name" VARCHAR(50), "age" INTEGER, "active" BOOLEAN ); INSERT INTO "my_table" ("id", "name", "age", "active") VALUES (1, 'Alice', 30, TRUE), (2, 'Bob', 28, FALSE);

MySQL bulk INSERT (no CREATE)

Input: name,city Alice,Paris Bob,Berlin

Output: INSERT INTO `my_table` (`name`, `city`) VALUES ('Alice', 'Paris'), ('Bob', 'Berlin');

SQLite single-row mode

Input: k,v foo,1 bar,2

Output: INSERT INTO "my_table" ("k", "v") VALUES ('foo', 1); INSERT INTO "my_table" ("k", "v") VALUES ('bar', 2);

Tips

  • Always run CREATE TABLE separately first, verify the inferred types match your needs, then run the INSERTs — wrong types found mid-import are painful to fix.
  • MySQL booleans are TINYINT under the hood — the tool emits 0/1 literals which are universally accepted and avoid version-specific surprises.
  • Bulk INSERT (one statement) is much faster on large imports because of round-trip and parsing overhead, but a single bad row aborts the entire statement — use single-row mode for cleanup imports.
  • If your CSV has a non-comma delimiter (tab, pipe), check that no string values contain that delimiter — the parser handles quoted strings correctly only when CSV quoting rules were followed.
  • For very large CSVs (>50 MB), consider importing via your database's native loader (\copy in psql, LOAD DATA INFILE in MySQL) instead — those are far faster than INSERT statements.

Frequently Asked Questions

Is my CSV uploaded anywhere?▾
No. The CSV is parsed and converted entirely in your browser using plain JavaScript. Customer data, financial records, and exports with PII never leave your tab.
Can it handle CSVs with millions of rows?▾
It handles tens of thousands of rows comfortably. Above ~100K rows, browser memory and string concatenation slow down significantly — for larger imports use your database's native loader (psql \copy, MySQL LOAD DATA INFILE).
How does it handle CSV fields with commas, quotes, or newlines?▾
The parser implements RFC 4180 quoting: fields wrapped in double quotes can contain commas, newlines, and escaped double quotes (""). Make sure your source CSV follows the same convention.
What if a column has mixed types — some integers and some strings?▾
Type inference falls back to the most permissive type that fits all values. Mixed numbers and strings produce VARCHAR or TEXT, not INTEGER, so no data is lost.
How are NULL values represented?▾
Empty CSV fields become NULL in the generated INSERT statements. To insert an empty string instead, put "" (two double quotes) in the CSV.
Are duplicate column names handled?▾
They are sanitised but not deduplicated — if your CSV has two columns named the same after sanitisation, the generated INSERT will have duplicate column names and the database will reject it. Rename in the source first.
What about MySQL booleans being TINYINT?▾
This is intentional. The tool emits 0/1 literals for MySQL booleans, which is the safest representation across MySQL versions and storage engines. PostgreSQL emits TRUE/FALSE keywords.
How are column types like DATE or TIMESTAMP inferred?▾
They are not — date strings are emitted as VARCHAR/TEXT to preserve the exact source value. Convert them inside the database with CAST or ::DATE syntax after import for correct type semantics.

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Glossary

Bulk INSERT
A single SQL INSERT statement with multiple VALUES rows, e.g. INSERT INTO t VALUES (...), (...), (...). Faster than one INSERT per row for large imports because of reduced parsing and network overhead.
Identifier quoting
Wrapping a table or column name in delimiter characters so SQL parsers treat it as a name rather than a keyword. PostgreSQL and SQLite use double quotes; MySQL uses backticks.
Type inference
Determining a column's SQL type by examining the actual data — all integers means INTEGER, all true/false means BOOLEAN, etc. Used here to generate accurate CREATE TABLE statements from CSV alone.
Dialect
A specific database system's variant of SQL, with its own keywords, quoting style, and data types. The tool supports PostgreSQL, MySQL, and SQLite dialects with separate output rules for each.
VARCHAR(n)
A string column that can hold up to n characters. Sized in inferred CREATE TABLE statements to fit the longest value in the source data plus 50% headroom.
NULL value
The SQL absence-of-value marker. Empty CSV fields become NULL in the generated INSERT, distinguishing them from empty strings ''.