The JSON to SQL Converter turns a JSON array of objects into SQL INSERT statements you can run directly in your database. Paste your JSON, set a target table name, and get one statement per record with columns derived from the object keys. It removes the tedious, error-prone job of hand-writing repetitive inserts when seeding or migrating data.

How the Conversion Works

A JSON array of flat objects maps naturally onto a database table: each object is a row, and each key is a column. The converter collects the keys to build the column list, then generates an INSERT INTO table (columns) VALUES (...) statement for every object, quoting string values and leaving numbers unquoted so the SQL is valid.

From API Data to a Database

Data arrives as JSON constantly — from REST APIs, log exports, configuration dumps, and front-end forms — but relational databases speak SQL. Bridging the two by hand is slow and mistake-prone: you copy values, wrap strings in quotes, line up columns, and repeat for every record. Generating the statements automatically keeps the column order consistent, quotes values correctly, and scales effortlessly from three rows to three hundred, which is exactly what you want when seeding a database or loading fixtures.

Worked Example

Given this JSON:

[
  { "id": 1, "name": "Alice", "city": "London" },
  { "id": 2, "name": "Bob", "city": "Paris" }
]

with the table name users, the converter outputs:

INSERT INTO users (id, name, city) VALUES (1, 'Alice', 'London');
INSERT INTO users (id, name, city) VALUES (2, 'Bob', 'Paris');

Strings are quoted, numbers are not, and each object becomes a runnable statement.

Key Features

Converts JSON arrays into INSERT statements
Configurable target table name
Maps object keys to columns automatically
Copy or download the generated SQL
Runs entirely in your browser — no upload

Common Use Cases

Database seeding: populate a table from sample or fixture data.
Data migration: move API responses or exports into a relational database.
Testing: generate insert statements for test environments.
Prototyping: quickly load JSON data into SQL without an ORM.

Tips and Considerations

For the cleanest output, give the converter an array of flat objects that all share the same keys, so every row lines up with the same columns. If your objects contain nested objects or arrays, flatten them first, since a relational column expects a single value. Before running the generated SQL on real data, confirm the table already exists with matching column types, and consider wrapping large batches in a transaction so a single bad row does not leave the table half-populated. Always review generated statements before executing them against a production database.

FAQ

What JSON shape is expected?

An array of flat objects works best; each object becomes one row and its keys become columns.

Which SQL dialects work?

The generated INSERT syntax is standard and works across common databases such as MySQL, PostgreSQL, and SQLite.

Are strings quoted correctly?

Yes — string values are wrapped in quotes while numbers are left unquoted for valid SQL.

What about nested objects?

Flatten nested structures first, since a table column expects a single scalar value.

Can I set the table name?

Yes — provide any target table name for the generated statements.

Is my data uploaded?

No. Conversion happens locally in your browser.

Related Tools

Explore the JSON to TSV Converter for spreadsheets, or the TSV to JSON Converter for the reverse.

JSON to SQL Converter