AVRO Schema Generator
2025Generate AVRO schemas by describing the data structure
Schema Configuration
JSON Input
Avro Schema
Real-time Generation
Schemas are generated instantly as you type, with intelligent type inference and validation.
Advanced Type Handling
Supports complex nested objects, arrays, unions, and proper integer/long/double type detection.
Standards Compliant
Generated schemas follow Apache Avro specifications and are ready for production use.
How It Works
Follow these simple steps to generate your Avro schema
Input Your JSON Data
Paste your JSON data into the input box on the left. You can use the "Sample" button to see an example, or the "Format" button to beautify messy JSON. The tool accepts any valid JSON structure including nested objects, arrays, and mixed data types.
Configure Schema Settings
Set your desired schema name and namespace in the configuration panel. Choose whether to include nullable types (union with null) for optional fields. The tool will automatically sanitize names to follow Avro naming conventions.
Automatic Schema Generation
The tool analyzes your JSON structure and automatically generates the corresponding Avro schema. It intelligently maps JSON types to Avro types, handles nested objects as records, creates proper array definitions, and manages union types for mixed-type arrays.
Copy or Download Schema
Once generated, you can copy the schema to your clipboard or download it as a .avsc file. The schema is immediately ready for use in your Avro-based applications, Kafka producers/consumers, or data processing pipelines.
Features & Tips
Advanced features and best practices for optimal results
Key Features
Automatically distinguishes between int, long, and double based on value ranges and precision.
Handles deeply nested objects, arrays of objects, and mixed-type arrays with union types.
Instant JSON validation with helpful error messages and syntax highlighting.
Generated schemas follow Apache Avro specifications and naming conventions.
Pro Tips
For best results, provide JSON that includes all possible field variations and edge cases.
Enable "Include nullable types" if your data might have missing or null values.
Use your organization's standard naming convention (e.g., com.company.project.schemas).
Consider future changes when designing schemas. Add documentation and default values where appropriate.
About Apache Avro
Understanding Avro schemas and their benefits
Apache Avro is a data serialization system that provides rich data structures and a compact, fast binary data format. It's widely used in big data ecosystems, particularly with Apache Kafka, Hadoop, and Spark for efficient data storage and transmission.
Key Benefits
- Compact Binary Format: Significantly smaller than JSON or XML
- Schema Evolution: Backward and forward compatibility support
- Rich Data Types: Support for complex nested structures
- Language Agnostic: Code generation for multiple languages
Common Use Cases
- Data Pipelines: ETL processes and stream processing
- Message Queues: Kafka message serialization
- Data Storage: Hadoop, Parquet, and data lakes
- API Communication: Service-to-service data exchange