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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

Data formats

One of the important characteristics of any streaming solution is that it serves as an integration platform as well. It collects events from varied sources and performs processing on these different events to produce the desired outcomes. One of the pertinent problems with such integration platforms is different data formats. Each type of source has its own format. Some support XML formats and some support JSON or Avro formats. It is difficult for you to design a solution catering to all formats. Moreover, as more and more data sources get added, you need to add support for data formats supported by the newly added source. This is obviously a maintenance nightmare and buggy.

Ideally, your streaming solution should support one data format. Events should be in the key/value model. The data format for these key/value events should be one agreed-on format. You should...

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