How to Convert JSON to SQL: CREATE TABLE + INSERTs Across Dialects

You have a JSON array — an API dump, an export, some seed data — and you need it in a database. Converting JSON to SQL means more than gluing together INSERT statements: you need the right column types, correct escaping, the proper syntax for your database, and sensible batching. This guide shows how to do it well, online and in code.

The quickest way: convert JSON to SQL online

Copy a JSON array of objects, paste it into the JSON to SQL converter, choose your database, and press Convert to SQL. You get a CREATE TABLE plus batched INSERT statements to copy or download.

Prefer a client-side tool (ours runs entirely in your browser) so production data is never uploaded. There’s no file-size cap either.

Use it when: you want runnable SQL fast, without writing a loader script.

Pick the dialect — it matters

SQL is not one language. The same data needs different syntax per database:

• Identifier quoting: MySQL uses `backticks`, PostgreSQL and SQLite use "double quotes", SQL Server uses [brackets].
• Booleans: PostgreSQL has real TRUE/FALSE; MySQL, SQLite, and SQL Server use 1/0.
• Unicode text: SQL Server wants N'...' literals.

A converter that targets a single dialect will produce SQL that fails on the others. Ours generates correct output for MySQL, PostgreSQL, SQLite, and SQL Server.

Let it infer column types

Dumping everything as TEXT works but wastes space and breaks math and sorting. Good conversion scans each column and infers a type:

• all integers → INT / INTEGER
• any decimals → DOUBLE / REAL / FLOAT
• only true/false → BOOLEAN / TINYINT(1) / BIT
• nested objects/arrays → a JSON column (JSON, JSONB) or text
• anything else → TEXT / NVARCHAR(MAX)

That produces a CREATE TABLE you can actually use, with the types mapped per dialect.

Escape values and batch the inserts

Two correctness essentials:

• Escaping. A name like O'Brien must become 'O''Brien' (doubled quote), or your script breaks — or worse, becomes an injection vector. Never hand-concatenate; let the tool escape.
• Batching. One INSERT per row is slow for big datasets, but a single statement with 100,000 rows can exceed packet limits. Group rows into multi-row batches and size them to your database.

Handle nested data

Relational tables are flat. The pragmatic approach for nested objects and arrays is to store them as JSON text in a JSON/JSONB column (or TEXT on SQLite), which keeps the data without forcing a full schema. For heavily relational data, you’d normalize nested arrays into related tables with foreign keys — a bigger modeling exercise.

Convert JSON to SQL in Python

pandas can write rows straight to a database with the right types via SQLAlchemy:

import pandas as pd
from sqlalchemy import create_engine

df = pd.read_json("data.json")
engine = create_engine("postgresql://user:pass@localhost/db")
df.to_sql("data", engine, if_exists="replace", index=False)

to_sql infers column types and handles escaping. To emit a .sql file instead of loading directly, iterate the rows and build parameterized statements.

Which method should you use?

For quick, runnable SQL — with the right dialect, inferred types, and safe escaping — use the online JSON to SQL converter: paste, pick your database, and download. Use pandas + SQLAlchemy when you’re loading data inside a Python pipeline. Either way, choose the correct dialect and let the tool handle escaping and types.

Working with other formats? See how to convert JSON to CSV and JSON to Excel. New to the format? Read what is JSON.