schema-registry VS kcat

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, 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.

This deployment creates a kcat container we can use to produce and consume messages.

Now we don't see any additional output. To verify it worked, let's use kafkacat to consume the topic's events. (We install kafkacat in the Dev Container. Please run the following command in VSCode's terminal)

Before we come to an end here, let's explore one additional helpful tool: kcat (formerly known as kafkacat).

Let's look at the data in the ad_events topic from the Kafka broker and see if we can identify the problem. We'll install kcat (formerly kafkacat):

kcat (formerly KafkaCat) - https://github.com/edenhill/kcat

Start with multiple consumers and produce events (this gives a sense about consistency or need for reliable data) - Producer could be command line or kafkacat

kcat is an awesome tool to make our life easier, it allows us to read and write from kafka topics without tons of scripts and in a more user-friendly way.

kcat