This concludes our section on Kafka consumers. This chapter addresses one of the key functionalities of Kafka message flows. The major focus was on understanding consumer internal working and how the number of consumers in the same group and number of topic partitions can be utilized to increase throughput and latency. We have also covered how to create consumers using consumer APIs and how to handle message offsets in case consumer fails.
We started with Kafka consumer APIs and also covered synchronous and asynchronous consumers and their advantages and disadvantages. We explained how to increase the throughput of a consumer application. We then went through the consumer rebalancer concept and when it gets triggered and how we can create our own rebalancer. We also focused on different consumer patterns that are used in different consumer applications. We focused on...
Building Data Streaming Applications with Apache Kafka
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Building Data Streaming Applications with Apache Kafka
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Overview of this book
Apache Kafka is a popular distributed streaming platform that acts as a messaging queue or an enterprise messaging system. It lets you publish and subscribe to a stream of records, and process them in a fault-tolerant way as they occur.
This book is a comprehensive guide to designing and architecting enterprise-grade streaming applications using Apache Kafka and other big data tools. It includes best practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with ease. This book first takes you through understanding the type messaging system and then provides a thorough introduction to Apache Kafka and its internal details. The second part of the book takes you through designing streaming application using various frameworks and tools such as Apache Spark, Apache Storm, and more. Once you grasp the basics, we will take you through more advanced concepts in Apache Kafka such as capacity planning and security.
By the end of this book, you will have all the information you need to be comfortable with using Apache Kafka, and to design efficient streaming data applications with it.
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Building Data Streaming Applications with Apache Kafka
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Table of Contents (14 chapters)
Preface
Free Chapter
Introduction to Messaging Systems
Introducing Kafka the Distributed Messaging Platform
Deep Dive into Kafka Producers
Deep Dive into Kafka Consumers
Building Spark Streaming Applications with Kafka
Building Storm Applications with Kafka
Using Kafka with Confluent Platform
Building ETL Pipelines Using Kafka
Building Streaming Applications Using Kafka Streams
Kafka Cluster Deployment
Using Kafka in Big Data Applications
Securing Kafka
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