AD482 - ch05s10

Preface A: Introduction

Section A.1: Developing Event-Driven Applications with Apache Kafka and Red Hat AMQ Streams

Section A.2: Orientation to the Classroom Environment

Chapter 1: Designing Event-driven Applications

Section 1.1: Introducing Event-driven Applications

Section 1.2: Quiz: Introducing Event-driven Applications

Section 1.3: Guided Exercise: Configuring a Developer Environment

Section 1.4: Summary

Chapter 2: Introducing Kafka and AMQ Streams Concepts

Section 2.1: Describing Kafka and AMQ Streams

Section 2.2: Quiz: Describing Kafka and AMQ Streams

Section 2.3: Describing the Kafka Ecosystem and Architecture

Section 2.4: Quiz: Describing the Kafka Ecosystem and Architecture

Section 2.5: Creating Topics

Section 2.6: Guided Exercise: Creating Topics

Section 2.7: Sending Data with Producers

Section 2.8: Guided Exercise: Sending Data with Producers

Section 2.9: Receiving Data with Consumers

Section 2.10: Guided Exercise: Receiving Data with Consumers

Section 2.11: Defining Data Formats and Structures

Section 2.12: Guided Exercise: Defining Data Formats and Structures

Section 2.13: Lab: Introducing Kafka and AMQ Streams Concepts

Section 2.14: Summary

Chapter 3: Building Applications with the Streams API

Section 3.1: Processing Data Streams

Section 3.2: Guided Exercise: Processing Data Streams

Section 3.3: Describing Kafka Streams Architecture

Section 3.4: Quiz: Describing Kafka Streams Architecture

Section 3.5: Using Basic Kafka Streams Objects

Section 3.6: Guided Exercise: Using Basic Kafka Streams Objects

Section 3.7: Lab: Building Applications with the Streams API

Section 3.8: Summary

Chapter 4: Creating Asynchronous Services with Event Collaboration

Section 4.1: Implementing Stateless Event Processing

Section 4.2: Guided Exercise: Implementing Stateless Event Processing

Section 4.3: Implementing Stateful Event Processing

Section 4.4: Guided Exercise: Implementing Stateful Event Processing

Section 4.5: Partitioning Stream Processing for Scalability

Section 4.6: Guided Exercise: Partitioning Stream Processing for Scalability

Section 4.7: Lab: Creating Asynchronous Services with Event Collaboration

Section 4.8: Summary

Chapter 5: Integrating Data Systems with Kafka Connect

Section 5.1: Creating a Connect Cluster

Section 5.2: Guided Exercise: Creating a Connect Cluster

Section 5.3: Creating Connectors

Section 5.4: Guided Exercise: Creating Connectors

Section 5.5: Transforming Messages

Section 5.6: Guided Exercise: Transforming Messages

Section 5.7: Capturing Change Event Data with Debezium

Section 5.8: Guided Exercise: Capturing Change Event Data with Debezium

Section 5.9: Lab: Integrating Data Systems with Kafka Connect

SectionSection 5.10: Summary

Chapter 6: Troubleshooting AMQ Streams Applications

Section 6.1: Maintaining Message Ordering

Section 6.2: Guided Exercise: Maintaining Message Ordering

Section 6.3: Configuring Producer Retries and Idempotence

Section 6.4: Guided Exercise: Configuring Producer Retries and Idempotence

Section 6.5: Preventing Duplication and Data Loss

Section 6.6: Guided Exercise: Preventing Duplication and Data Loss

Section 6.7: Implementing Test Cases in Event-driven Applications

Section 6.8: Guided Exercise: Implementing Test Cases in Event-driven Applications

Section 6.9: Lab: Troubleshooting AMQ Streams Applications

Section 6.10: Summary

Chapter 7: Comprehensive Review of AD482

Section 7.1: Comprehensive Review

Section 7.2: Lab: Comprehensive Review of AD482

Appendix A: Creating a Quay Account

Section A.1: Creating a Quay Account

Appendix B: Creating a GitHub Account

Section B.1: Creating a GitHub Account

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Summary

In this chapter, you learned:

Kafka Connect is a tool for reliable data streaming between Apache Kafka and external systems.
In AMQ Streams on the Red Hat OpenShift Container Platform (RHOCP), you can create or configure a Kafka Connect cluster by using the KafkaConnect custom resource.
Kafka Connect connectors are the components that integrate Kafka Connect with external technologies. You can create connectors in AMQ Streams on RHOCP by using the KafkaConnector resource, or by using the Kafka Connect REST API.
Single Message Transformation (SMT) is an interface inside Kafka Connect to apply stateless transformations to the individual messages of your integration pipeline.
Data inconsistencies with dual writes might occur in microservices that use a database per service model for persistence, because some external components might not support transactional operations.
The Change Data Capture pattern with Debezium provides a Kafka Connect connector implementation that enables tracking and monitoring the data change in supported databases.
Using the Debezium CDC with the Outbox pattern provides a solution for dual writes and for the data inconsistencies between microservices databases.

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Welcome to the Developing Event-Driven Applications with Apache Kafka and Red Hat AMQ Streams group!

cschunke

26 wrz 2023

We are excited to launch a space dedicated to the Red Hat Training course Developing Event-Driven Applications with Apache Kafka and Red Hat AMQ Streams! To gain the most value from this group - click the "Join Group" button in the upper right hand corner of the group home page.We encourage group members to collaborate in this group to discuss topics, ask questions, share best practices and tips, provide course feedback, and share their accomplishments as it relates to AD482.Read more about Developing Event-Driven Applications with Apache Kafka and Red Hat AMQ Streams here.

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