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Course Outline

Introduction

  • Kafka compared to Spark, Flink, and Storm

Overview of Kafka Streams Features

  • Stateful and stateless processing, event-time processing, DSL, event-time-based windowing operations, and more

Case Study: Kafka Streams API for Predictive Budgeting

Setting Up the Development Environment

Creating a Streams Application

Starting the Kafka Cluster

Preparing Topics and Input Data

Options for Processing Stream Data

  • High-level Kafka Streams DSL
  • Lower-level Processor API

Transforming Input Data

Inspecting Output Data

Stopping the Kafka Cluster

Options for Deploying the Application

  • Classic operations tools (Puppet, Chef, and Salt)
  • Docker
  • WAR file

Troubleshooting

Summary and Conclusion

Requirements

  • Understanding of Apache Kafka
  • Experience in Java programming
 7 Hours

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