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Building Data Streaming Applications with Apache Kafka

You're reading from   Building Data Streaming Applications with Apache Kafka Design, develop and streamline applications using Apache Kafka, Storm, Heron and Spark

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787283985
Length 278 pages
Edition 1st Edition
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Authors (2):
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Chanchal Singh Chanchal Singh
Author Profile Icon Chanchal Singh
Chanchal Singh
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Messaging Systems FREE CHAPTER 2. Introducing Kafka the Distributed Messaging Platform 3. Deep Dive into Kafka Producers 4. Deep Dive into Kafka Consumers 5. Building Spark Streaming Applications with Kafka 6. Building Storm Applications with Kafka 7. Using Kafka with Confluent Platform 8. Building ETL Pipelines Using Kafka 9. Building Streaming Applications Using Kafka Streams 10. Kafka Cluster Deployment 11. Using Kafka in Big Data Applications 12. Securing Kafka 13. Streaming Application Design Considerations

Deep Dive into Kafka Consumers

Every messaging system has two types of data flows. One flow pushes the data to the Kafka queues and the other flow reads the data from those queues. In the previous chapter, our focus was on the data flows that are pushing the data to Kafka queues using producer APIs. After reading the previous chapter, you should have sufficient knowledge about publishing data to Kafka queues using producer APIs in your application. In this chapter, our focus is on the second type of data flow--reading the data from Kafka queues.

Before we start with a deep dive into Kafka consumers, you should have a clear understanding of the fact that reading data from Kafka queues involves understanding many different concepts and they may differ from reading data from traditional queuing systems.

With Kafka, every consumer has a unique identity and they are in full control...
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