Can Avrotize infer schemas from existing data files?

Yes, it features schema inference capabilities, allowing you to generate Avro or JSON Structure schemas from existing JSON, XML, or CSV data files.

Which schema formats does Avrotize support?

It converts between Avro, JSON Schema, XSD, Protobuf, ASN.1, Parquet, and numerous database schemas like SQL (MySQL, PostgreSQL), Kusto, MongoDB, and Elasticsearch.

What programming languages can Avrotize generate code for?

Avrotize can generate code for Python, C#, Java, TypeScript, Go, Rust, C++, and JavaScript directly from your schema definitions.

Avrotize is a versatile 'Rosetta Stone' for data structure definitions, converting schemas between various formats and generating code for multiple programming languages.

How does Avrotize integrate with other development tools?

Avrotize operates as a local MCP (Metaverse Client Protocol) server, exposing its powerful schema conversion and code generation tools to external MCP clients for enhanced integration.

Avrotize

Name: Avrotize
Author: clemensv

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Convert data structure definitions between various schema formats and generate code for multiple programming languages using Apache Avro Schema as an intermediary.

Avrotize acts as a universal translator for data structure definitions, enabling seamless conversion between a wide array of data and database schema formats. It uses its internal Avrotize Schema, a superset of Apache Avro Schema, as a central pivot point for all conversions. This capability allows developers to 'unlock' data definitions from specific formats like JSON Schema or XML Schema, making them portable across different contexts and facilitating code generation for popular programming languages such as Python, C#, Java, and TypeScript. The tool focuses on defining data structures for messaging, eventing, serialization, and database tables, rather than converting data instances or modeling object-oriented inheritance.

主な機能

01Operates as a local MCP server, exposing its schema conversion and code generation tools to external MCP clients for enhanced integration.

02Schema inference capabilities, allowing the generation of Avro or JSON Structure schemas from existing JSON, XML, or CSV data files.

03116 GitHub stars

04Bidirectional conversion between numerous data schema formats including Avro, JSON Schema, XSD, Protobuf, ASN.1, and Parquet.

05Conversion to and from various database schema formats such as SQL (MySQL, PostgreSQL, Oracle), Kusto, MongoDB, Cassandra, and Elasticsearch.

06Code generation for popular programming languages like Python, C#, Java, TypeScript, Go, Rust, C++, and JavaScript from schema definitions.

ユースケース

01Automating the generation of boilerplate data classes in multiple programming languages from a single, canonical schema definition.

02Validating JSON instances against defined Avro or JSON Structure schemas to ensure data integrity.

03Migrating and standardizing data structure definitions across diverse data exchange, storage, and application environments.

04Facilitating the integration and interoperability of systems by translating schema definitions between incompatible formats.