Learn the method to integrate Kafka with Spark for consuming streaming data and discover how to unleash your streaming analytics needs.
Kafka is a messaging broker system that facilitates the passing of messages between producer and consumer. On the other hand, Spark Structure streaming consumes static and streaming data from various sources (like Kafka, Flume, Twitter, etc.) that can be processed and analyzed using a high-level algorithm for Machine Learning and pushes the result out to an external storage system. The main advantage of structured streaming is to get continuous incrementing of the result as the streaming data continue to arrive.
Kafka has its own stream library and is best for transforming Kafka topic-to-topic whereas Spark streaming can be integrated with almost any type of system. For more detail, you can refer to this blog.
In this blog, I’ll cover an end-to-end integration of Kafka with Spark structured streaming by creating Kafka as a source and Spark structured streaming as a sink.
Let’s create a Maven project and add following dependencies in
pom.xml.Now, we will be creating a Kafka producer that produces messages and pushes them to the topic. The consumer will be the Spark structured streaming DataFrame.
First, setting the properties for the Kafka producer.
read more at: https://morioh.com/p/33ee2699c283/integrating-kafka-with-spark-structured-streaming
