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HBase High Performance Cookbook

You're reading from   HBase High Performance Cookbook Solutions for optimization, scaling and performance tuning

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781783983063
Length 350 pages
Edition 1st Edition
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Author (1):
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Ruchir Choudhry Ruchir Choudhry
Author Profile Icon Ruchir Choudhry
Ruchir Choudhry
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Table of Contents (13) Chapters Close

Preface 1. Configuring HBase 2. Loading Data from Various DBs FREE CHAPTER 3. Working with Large Distributed Systems Part I 4. Working with Large Distributed Systems Part II 5. Working with Scalable Structure of tables 6. HBase Clients 7. Large-Scale MapReduce 8. HBase Performance Tuning 9. Performing Advanced Tasks on HBase 10. Optimizing Hbase for Cloud 11. Case Study Index

Machine learning using Hbase


Before we dive deep into the details of Hbase/Hadoop, Mahout, and machine learning, it's vital to discuss and highlight some important concepts, which will be used in this chapter.

Data science—in software engineering terms—is an operation of a set of programs that churns a large quantity of data to evaluate supervised or unsupervised learning models and provides a valuable tool to data scientists or systems through which decisions can be made.

The most important aspect is applying programming/algorithms that provide a cleaner dataset on which the next program can be synthesized. This also allows us to look at different patterns that can change as the size of data grows. Thus, it's important to use some form of machine learning that can adapt as the size of data grows exponentially.

There are various use cases that can be solved with different types of machine learning techniques:

  • Supervised learning

  • Unsupervised learning

  • Recommender system

  • Model efficacy

The use of...

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