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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
Languages
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

Hadoop clusters

So far, we have seen how to create a single Hadoop cluster using Ambari. But, is there ever a requirement for multiple Hadoop clusters?

The answer depends on the business requirements. There are trade-offs for both single versus multiple Hadoop clusters.

Before we jump into the advantages and disadvantages of both of these, let's see in what scenarios we might use either.

A single cluster for the entire business

This is the most straightforward approach and every business starts with one cluster, at least. As the diversity of the business increases, organizations tend to choose one cluster per department, or business unit.

The following are some of the advantages:

  • Ease of operability: Since there is only...
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