JSONToTable

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Generate Avro Schemas from JSON - Complete Guide

Step 1

Input Your JSON Data

Get your JSON data ready for schema generation. You have several convenient options:

•
Paste directly: Copy your JSON data from any source
•
Upload file: Click the Upload button to select a .json file
•
Try sample: Click the Sample button to see a working example
•
Configure schema: Set custom schema name and namespace for your organization

JSON input example:

{
  "id": 1,
  "name": "John Doe",
  "email": "[email protected]",
  "active": true
}
Step 2

Automatic Schema Generation

Watch as your JSON is transformed into a valid Apache Avro schema. The generator provides:

•
Smart type inference: Automatically detects int, long, float, double, string, boolean types
•
Nested structures: Handles complex nested objects and arrays as Avro records
•
Kafka ready: Compatible with Kafka Schema Registry and Confluent Platform
•
Official library: Uses avsc library for accurate schema generation

Generated Avro schema example:

{
  "type": "record",
  "name": "User",
  "namespace": "example.avro",
  "fields": [
    { "name": "id", "type": "int" },
    { "name": "name", "type": "string" },
    { "name": "email", "type": "string" },
    { "name": "active", "type": "boolean" }
  ]
}
Step 3

Download or Copy Schema

Once generated, download the schema as an .avsc file or copy it to your clipboard. Use it in:

•
Apache Kafka: Register schema with Kafka Schema Registry for data streaming
•
Hadoop ecosystem: Use with Hive, Pig, Spark for big data processing
•
Data pipelines: Define schemas for ETL processes and data validation

FAQs About Avro Schema Generation

What is an Avro schema?

An Avro schema is a JSON document that defines the structure of your data. It specifies field names, types, and documentation. Schemas enable schema evolution, data validation, and efficient binary serialization in systems like Apache Kafka and Hadoop.

How does automatic type inference work?

The generator uses the official avsc library to analyze your JSON data and infer the most appropriate Avro types. Integers become int, decimals become double, and nested objects become record types with proper namespacing.

Can I use this schema with Kafka?

Yes! Generated schemas are fully compatible with Apache Kafka Schema Registry and Confluent Platform. Download the .avsc file and register it with your schema registry using the REST API or CLI tools.

What Avro types are supported?

The generator supports all Avro primitive types (int, long, float, double, string, boolean, bytes, null) and complex types (records, arrays, maps, unions, enums, fixed). It handles nested structures and automatically creates proper record definitions with namespaces.

What other Avro tools are available?

We offer a complete Avro toolkit: JSON to Avro converter for encoding data, Avro to JSON for decoding, and more tools for working with Apache Avro in your data pipelines.

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