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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
Tools
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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

Summary

We walked you through some of the aspects of using Kafka in big data applications. By the end of this chapter, you should have clear understanding of how to use Kafka in big data Applications. Volume is one of the important aspects of any big data application. Therefore, we have a dedicated section for it in this chapter, because you are required to pay attention to granular details while managing high volumes in Kafka. Delivery semantics is another aspect you should keep in mind. Based on your choice of delivery semantics, your processing logic would differ. Additionally, we covered some of the best ways of handling failures without any data loss and some of the governance principles that can be applied while using Kafka in big data pipeline. We gave you an understanding of how to monitor Kafka and what some of the useful Kafka matrices are. You learned a good detail...

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