Benefits of using Schema Registry with CloudKarafka
CloudKarafka offers Apache Kafka as a service, meaning that we provide users with a two-minute server setup of Apache Kafka followed by management and support. However, the usage of Apache Kafka varies greatly. To help you wade through all the information, CloudKarafka presents this guide on how to start using Schema Registry with CloudKarafka.
Apache Kafka is built to handle a large amount of data at a rapid pace, and one of the benefits is the internal design, which allows data to be read and reread without stressing the broker with large CPU requirements. Apache Kafka Schema registry operates like a serving layer to further ensure that data can be transported seamlessly between producers and consumers, making sure the formats from the sending and the receiving applications match.
How the schema registry works
- The producer creates a message that contains schema and data.
- The Schema Registry takes the data, serializes it according to a cache of schemas and identifications it (Schema Registry) has registered and kept.
- The consumer, with its own schema (the one in which it is expecting the message to conform), receives the payload of data, deserializes it, and, based on id, looks up the full schema from the cache or Schema Registry.
- A compatibility check is performed with three possible outcomes: a) Schemas match, which is an instant success! Messages are now delivered to the consumer. b) Schemas don’t match, but the message is compatible. So the payload transformation (schema evolution) converts the schema and messages are delivered! c) Schemas don’t match, the message is not compatible, so the message fails to be delivered.
We hope that you found this information useful. If you have any questions or concerns regarding this blog post, send an email to firstname.lastname@example.org.
All the best,
The CloudKarafka Team