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Mastering Hadoop

You're reading from   Mastering Hadoop Go beyond the basics and master the next generation of Hadoop data processing platforms

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783983643
Length 374 pages
Edition 1st Edition
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Author (1):
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Sandeep Karanth Sandeep Karanth
Author Profile Icon Sandeep Karanth
Sandeep Karanth
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Table of Contents (15) Chapters Close

Preface 1. Hadoop 2.X FREE CHAPTER 2. Advanced MapReduce 3. Advanced Pig 4. Advanced Hive 5. Serialization and Hadoop I/O 6. YARN – Bringing Other Paradigms to Hadoop 7. Storm on YARN – Low Latency Processing in Hadoop 8. Hadoop on the Cloud 9. HDFS Replacements 10. HDFS Federation 11. Hadoop Security 12. Analytics Using Hadoop A. Hadoop for Microsoft Windows Index

Data analytics workflow

Data analytics involves transforming and inspecting data to figure out the inherent meaningful information from it. The information extracted is used in decision making or suggesting conclusions. The analytics workflow is shown in the following diagram:

Data analytics workflow

The steps involved in the analytics workflow are as follows:

  1. The first step is to identify the problem to be solved. This is important as the decisions in the rest of the steps hinge on it. For example, the problem statement will dictate what kind of data to collect and what the important features that represent the solution to the problem are. A lot of domain expertise is required in data analytics, and a problem space where expertise is accessible is almost mandatory.
  2. Once the problem is identified, appropriate data needs to be collected. The collected data needs to be represented in a format that optimizes on space without losing resolution in information. Enterprises now need to be aware of compliance and security...
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