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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 FREE CHAPTER 2. Hadoop Life Cycle Management 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

Building a Hadoop cluster in the Cloud

We saw earlier that the Cloud offers a flexible and easy way to rent resources such as servers, storage, networking, and so on. The Cloud has made it very easy for consumers with the pay-as-you-go model, but much of the complexity of the Cloud is hidden from us by the providers.

In order to better understand whether Hadoop is well suited to being on the Cloud, let's try to dig further and see how the Cloud is organized internally.

At the core of the Cloud are the following mechanisms:

  • A very large number of servers with a variety of hardware configurations
  • Servers connected and made available over IP networks
  • Large data centers to host these devices
  • Data centers spanning geographies with evolved network and data center designs

If we pay close attention, we are talking about the following:

  • A very large number of different CPU architectures...
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