kafkajs VS ApacheKafka

I will use the KafkaJS library to achieve it quickly. Let’s just paste the code here.

The streaming database writes back results to Kafka topics by using sink operation in real-time. Now, we need to add JavaScript code to consume and process discount messages received from Kafka and update the UI in real-time to display them. We can use the KafkaJS library, which is a popular Kafka client for Node.js to listen and consume Kafka messages.

Before we start producing and consuming events, we need to install Kafka to our API. We will use the Kafka.js client library for Node.js.

Once Kafka runs on Kube-native support that Strimzi offers, you can now start utilizing Node.js to create your streams and use libraries such as KafkaJS, a modern Apache Kafka client for Node.js.

Senden an Kafka (d. h. Senden der Daten an Apache Kafka)

Stream-processing platforms such as Apache Kafka, Apache Pulsar, or Redpanda are specifically engineered to foster event-driven communication in a distributed system and they can be a great choice for developing loosely coupled applications. Stream processing platforms analyze data in motion, offering near-zero latency advantages. For example, consider an alert system for monitoring factory equipment. If a machine's temperature exceeds a certain threshold, a streaming platform can instantly trigger an alert and engineers do timely maintenance.

Apache Kafka is a distributed streaming platform capable of handling high throughput of data, while ReductStore is a databases for unstructured data optimized for storing and querying along time.

*Push data *(original source image, GPS, timestamp) in a common place (Apache Kafka,...)

RabbitMQ comes with administrative tools to manage user permissions and broker security and is perfect for low latency message delivery and complex routing. In comparison, Apache Kafka architecture provides secure event streams with Transport Layer Security(TLS) and is best suited for big data use cases requiring the best throughput.

Apache Kafka is a distributed streaming platform to share data between applications and services in real-time.

also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.