Course Outline


  • Kafka vs Spark, Flink, and Storm

Overview of Kafka Streams Features

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

Case Study: Kafka Streams API for Predictive Budgeting

Setting up the Development Environment

Creating a Streams Application

Starting the Kafka Cluster

Preparing the Topics and Input Data

Options for Processing Stream Data

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

Transforming the Input Data

Inspecting the Output Data

Stopping the Kafka Cluster

Options for Deploying the Application

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


Summary and Conclusion


  • An understanding of Apache Kafka
  • Java programming experience
  7 Hours


Related Courses

Spark Streaming with Python and Kafka

  7 hours

Confluent KSQL

  7 hours

Apache Ignite for Developers

  14 hours

Unified Batch and Stream Processing with Apache Beam

  14 hours

Apache Apex: Processing Big Data-in-Motion

  21 hours

Apache Storm

  28 hours

Apache NiFi for Administrators

  21 hours

Apache NiFi for Developers

  7 hours

Apache Flink Fundamentals

  28 hours

Samza for Stream Processing

  14 hours

Tigon: Real-time Streaming for the Real World

  14 hours

Real-Time Stream Processing with MapR

  7 hours

A Practical Introduction to Stream Processing

  21 hours

Building Kafka Solutions with Confluent

  14 hours

Apache Kafka for Python Programmers

  7 hours