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Course Outline
Introduction
- 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
Troubleshooting
Summary and Conclusion
Requirements
- An understanding of Apache Kafka
- Java programming experience
7 Hours
Testimonials (2)
The explanations were very good, although some questions could have been avoided if those points had been addressed at the beginning of the topics. A good command and experience in the subject were noted.
Alan Jaime Rodriguez Garcia - BANCO DE MEXICO
Course - Stream Processing with Kafka Streams
Machine Translated
Recalling/reviewing keypoints of the topics discussed.
