schema-registry VS obm_confluent_blog

So, is JR yet another faking library written in Go? Yes and no. JR indeed implements most of the APIs in fakerjs and Go fake it, but it's also able to stream data directly to stdout, Kafka, Redis and more (Elastic and MongoDB coming). JR can talk directly to Confluent Schema Registry, manage json-schema and Avro schemas, easily maintain coherence and referential integrity. If you need more than what is OOTB in JR, you can also easily pipe your data streams to other cli tools like kcat thanks to its flexibility.

Check here for more details. https://github.com/confluentinc/schema-registry

Confluent Schema Registry for Apache Kafka [GitHub]

So that means we can configure the Kafka producer and consumer with an imaginary schema registry url, that only needs to start with “mock://” and you automatically get to work with the MockSchemaRegistryClient. This way you don't need to explicitly initiate the MockSchemaRegistryClient and configure everything accordingly. That also eradicates the need for the Confluent Schema Registry Container. Running the Kafka Testcontainer with the embedded Zookeeper, we no longer need an extra Zookeeper container and we are down to one Testcontainer for the messaging. This way I ended up with only two Testcontainers: Kafka and the database.

Confluent is a company founded by the creators of Apache Kafka. They are providing the Confluent Platform which consists of several components, all based on Kafka. The license for these components vary. The Schema Registry has the community-license, which basically means it's free to use as long as you don't offer the Schema Registry itself as a SaaS solution. The source code can be found on Github.

Schema Registry from Confluent (GitHub) => http://localhost:8081/

So what is actually the Schema Registry? And how does it help to make sense of binary data? In essence Schema Registry is an application with some Rest endpoints, from which schema's can be registered and retrieved. It used to only support Apache Avro. Later support for Protobuf and JSON Schema was added. Part of the same Github project, and what makes Schema Registry easy to use, is a Collection of Java classes that are used to go from bytes to typed objects and vice versa. There are several classes that support Kafka Streams and ksqlDB next to the more low level Kafka [Producer(https://kafka.apache.org/documentation/#producerapi) and Consumer clients. There are more advanced use cases, but basically you supply the url for the Schema Registry, and the library will handle the rest. For producing data this will mean optionally register a new schema too get the correct id. The consumer will use the encoded id to fetch the schema used to produce the data. It can also be used with other frameworks like Spring Cloud Stream. For example in the Kotlin Command Handler by using the SpecificAvroSerde class. You might need to set additional properties to get this working.