JSON to SQL Generator

Convert JSON objects to SQL CREATE TABLE statements with automatic type inference. Generate database schemas from JSON data with support for multiple SQL dialects.

Input JSON

Paste your JSON object or array
🔧 All JSON Tools

Generated SQL

CREATE TABLE and INSERT statements
SQL statements will appear here...

Key Features

  • 🗄️ Convert JSON objects to SQL CREATE TABLE statements
  • 🔍 Automatic SQL data type inference from JSON values
  • 🗃️ Support for multiple SQL dialects (MySQL, PostgreSQL, SQLite, SQL Server)
  • 📊 Generate sample INSERT statements from data
  • 🔧 Flatten nested JSON objects into columns
  • 📋 Handle JSON arrays intelligently
  • 💾 Download generated SQL as .sql file
  • 🔒 Privacy-focused: no data sent to servers
  • ⚡ Real-time SQL generation

Complete Guide to JSON to SQL Conversion

Converting JSON data to SQL CREATE TABLE statements is a fundamental task in modern database design and development. This comprehensive guide explains how to transform your JSON objects into proper database schemas, handle data type mapping, and create production-ready SQL statements for any database system.

🎯 When to Use JSON to SQL Generator

Database Migration

Migrating data from NoSQL databases (MongoDB, DocumentDB) to relational databases (MySQL, PostgreSQL) by converting JSON documents to normalized table structures.

API Data Import

Creating database schemas from REST API responses, enabling you to store API data in relational format for analytics and reporting.

Schema Prototyping

Rapidly prototype database schemas from sample JSON data during the early stages of application development and database design.

Data Integration

Integrating JSON data from external systems, microservices, or third-party APIs into your existing relational database infrastructure.

📚 Step-by-Step Tutorial

1. Prepare Your JSON Data

Start with clean, representative JSON data. If you have nested objects, decide whether to flatten them or store as JSON columns:

{ "user_id": 1001, "name": "Alice Johnson", "email": "alice@company.com", "registration_date": "2024-01-15T09:30:00Z", "is_premium": true, "profile": { "age": 28, "location": "San Francisco" } }

2. Configure Generation Options

  • SQL Dialect: Choose your target database (MySQL, PostgreSQL, SQLite, SQL Server)
  • Table Name: Use descriptive names following your naming conventions (e.g., users, customer_profiles)
  • Nested Objects: Enable flattening to create separate columns (profile_age, profile_location)
  • INSERT Statements: Generate sample data insertion queries for testing

3. Review Generated Schema

The tool automatically infers appropriate SQL data types, column constraints, and generates both CREATE TABLE and INSERT statements. Always review the output for your specific requirements.

🗄️ Database Compatibility & Differences

Feature MySQL PostgreSQL SQLite SQL Server
JSON Support JSON column type JSONB (binary) TEXT (as string) NVARCHAR(MAX)
Boolean Type BOOLEAN BOOLEAN INTEGER (0/1) BIT
Timestamp Format TIMESTAMP TIMESTAMP TEXT DATETIME2
Text Columns VARCHAR, TEXT VARCHAR, TEXT TEXT NVARCHAR

🔄 Data Type Mapping Guide

🔢 Numbers

Integers: → INTEGER/INT
Decimals: → DECIMAL(10,2)
Large numbers: → BIGINT

📝 Strings

Short text: → VARCHAR(100)
Medium text: → VARCHAR(255)
Long text: → TEXT

🕒 Dates

ISO 8601: → TIMESTAMP
Date only: → DATE
Unix timestamp: → INTEGER

✅ Booleans

MySQL/PostgreSQL: → BOOLEAN
SQLite: → INTEGER
SQL Server: → BIT

🚀 Common Use Cases & Best Practices

📊 Analytics & Reporting

Convert JSON logs, events, or metrics into structured tables for business intelligence tools like Tableau, Power BI, or custom dashboards. Flatten nested event properties into separate columns for easier querying and aggregation.

-- Example: Converting event tracking data {"event": "page_view", "user_id": 123, "page": "/dashboard", "timestamp": "2024-01-15T10:30:00Z"} ↓ CREATE TABLE page_views (event VARCHAR(50), user_id INTEGER, page VARCHAR(255), timestamp TIMESTAMP);

🔄 Data Warehousing

Transform JSON data from various sources (APIs, microservices, external systems) into a unified data warehouse schema. Essential for ETL processes and maintaining data consistency across different platforms.

  • Standardize data types across different JSON sources
  • Create staging tables for data validation
  • Design fact and dimension tables from JSON documents

🔧 Development & Testing

Quickly generate database schemas for development environments, create test data structures, and prototype new features. Especially useful when working with dynamic JSON APIs that need to be persisted in relational format.

  • Generate CREATE TABLE statements for new features
  • Create test databases with realistic data structures
  • Prototype schema changes before production deployment

🔧 Troubleshooting & FAQ

❓ Why are my nested objects not creating separate columns?

Make sure the "Flatten nested objects" option is enabled in the configuration panel. When disabled, nested objects are stored as JSON/TEXT columns. For complex nested structures, consider manually designing your schema to avoid overly wide tables.

❓ How should I handle large JSON datasets?

For large datasets (>100MB), consider processing data in chunks. Use array slicing to work with smaller samples first, then batch process the full dataset. Enable database indexing on frequently queried columns for better performance.

❓ What if my JSON has inconsistent data types?

The tool uses the most permissive data type when conflicts are detected. For example, if a field contains both numbers and strings, it defaults to VARCHAR. Review the generated schema and adjust data types manually if needed for your specific use case.

❓ How do I handle JSON arrays in my data?

Arrays are stored as JSON/TEXT columns by default. For relational normalization, consider creating separate tables for array elements with foreign key relationships. Use the array splitter tool for complex array handling scenarios.

❓ Can I customize the generated SQL syntax?

The tool generates standard SQL compatible with major databases. For custom syntax, constraints, or advanced features like triggers and indexes, use the generated SQL as a starting point and modify it in your preferred SQL editor or database management tool.

💡 Pro Tip: Schema Design Best Practices

Always validate your generated schemas with sample data before production deployment. Consider adding primary keys, foreign key relationships, and appropriate indexes based on your query patterns. Use meaningful column names and follow your organization's naming conventions.

🔑
Primary Keys
Add AUTO_INCREMENT IDs
📋
Constraints
NOT NULL, UNIQUE, CHECK
⚡
Indexes
Optimize query performance