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Modern Big Data Processing with Hadoop

You're reading from   Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Length 394 pages
Edition 1st Edition
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Concepts
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Authors (3):
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Manoj R Patil Manoj R Patil
Author Profile Icon Manoj R Patil
Manoj R Patil
Prashant Shindgikar Prashant Shindgikar
Author Profile Icon Prashant Shindgikar
Prashant Shindgikar
V Naresh Kumar V Naresh Kumar
Author Profile Icon V Naresh Kumar
V Naresh Kumar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Enterprise Data Architecture Principles 2. Hadoop Life Cycle Management FREE CHAPTER 3. Hadoop Design Consideration 4. Data Movement Techniques 5. Data Modeling in Hadoop 6. Designing Real-Time Streaming Data Pipelines 7. Large-Scale Data Processing Frameworks 8. Building Enterprise Search Platform 9. Designing Data Visualization Solutions 10. Developing Applications Using the Cloud 11. Production Hadoop Cluster Deployment

Real-time streaming components

In the following sections we will walk through some important real-time streaming components.

Message queue

The message queue lets you publish and subscribe to a stream of events/records. There are various alternatives we can use as a message queue in our real-time stream architecture. For example, there is RabbitMQ, ActiveMQ, and Kafka. Out of these, Kafka has gained tremendous popularity due to its various unique features. Hence, we will discuss the architecture of Kafka in detail. A discussion of RabbitMQ and ActiveMQ is beyond the scope of this book.

So what is Kafka?

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