Intro to Streaming with Kafka

Whenever you need to handle data as it comes in, you're looking into near-real-time and stream processing. While several technologies offer an API that abstracts the differences between batch and stream processing, like Apache Spark and Apache Flink, you would often still need a place to buffer the incoming data, at least if you want a robust system.

Apache Kafka is the industry standard. Many of the concepts you would learn from understanding how Kafka works, are immediately transferable to managed services like Azure Eventhubs and AWS Kinesis.

In this course, you will learn how to position Kafka and how it compares to other technologies. You will get an extended answer on when and when not to use Kafka and how it typically matures in an organisation. We will look at Kafka workflows to provide context for all the core concepts that we will cover.

Kafka is a streaming platform. Next to the core Kafka concepts, we will also do a deep dive on the schema registry and Kafka connect. Practical experience will be shared on how to best use those components.
Next, we look at stream processing and how streaming applications are typically built with events and Kafka.
Finally we will look at Kafka security best practices, when to start applying Kafka security and how to deal with this within an organisation.

By the end of this course, you’ll be prepared to achieve scalability, fault tolerance and durability with Apache Kafka. You will have a good overview of the complete Kafka ecosystem and know where and how to apply each component.

The course is currently not available yet in a flipped-classroom training.