Wire VS Apache Avro

It is long over due. I used Wire for my previous projet to generate Kotlin files from proto.

A schema.json converter for easier ingestion (likely supporting Avro and Protobuf)

Security Aware Data Metadata Data schema formats such as Avro and Json currently lack built-in support for data sensitivity or security-aware metadata. Additionally, common formats like Parquet and Iceberg, while efficient for storing large datasets, don’t natively include security-aware metadata. At Jarrid, we are exploring various metadata formats to incorporate data sensitivity and security-aware attributes that can be easily tracked at the data lake, file, and even down to the field level. By tagging security-aware metadata directly into the data schema, tracking encryption keys and enforcing security policies across data pipelines and storage becomes easier. Currently, Keyper makes encryption key permissions configuration-driven and trackable, ensuring that encryption keys and their access rules are well-managed, version-controlled, and easy to audit.

Apache AVRO [1] is one but it has been largely replaced by Parquet [2] which is a hybrid row/columnar format

[1] https://avro.apache.org/

The most common format for describing schema in this scenario is Apache Avro.

I am testing a new schema registry which loads and retrieves different kinds of avro schemas. In the process of testing, I need to create a bunch of different types of avro schemas. As it involves a lot of permutations, I decided to create the schema programmatically.I am using the apache avro SchemaBuilder to do so.

Other serialization alternatives have a schema validation option: e.g., Avro, Kryo and Protocol Buffers. Interestingly enough, gRPC uses Protobuf to offer RPC across distributed components:

Apache Avro is a data serialization system, for more information visit Apache Avro

Once things like JSON became more popular Apache Avro appeared. You can define Avro files which can then be generated into Python, Java C, Ruby, etc.. classes.