Data Analytics With Hadoop

Author: Benjamin Bengfort & Jenny Kim
Publisher: O'Reilly
Pages: 150
ISBN: 978-1491913703
Print: 1491913703
Kindle: B01GGQKXO4
Audience: Data Scientists familiar with Python

Rating: 4.5
Reviewer: Kay Ewbank

 

A book that is short and to the point - recommended

This is a book that concentrates on using Hadoop for data analysis rather than wasting time on deployment and management of Hadoop. It shows how to work in Python with MapReduce and Spark, Hive and HBase. 

 

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The first half of the book takes a high level view of distributed computing and aims to tell you how to run computations on a cluster. The second half then looks at the tools and techniques you might use, along with an explanation of why particular types of analysis and techniques are useful.

Having introduced the concept of the data product, the authors introduce the core concepts of Hadoop family, focusing on YARN and HDFS.

By Chapter 3, Bengfort and Kim get to MapReduce, and in particular how to write MapReduce jobs in Python (as the MapReduce API is written in Java). Soark is next to be introduced, which is the choice for everyday interactions and analysis on a Hadoop cluster.

Chapter 5 takes a practical look at how to write distributed data analysis jobs. The authors say that coming into this chapter, you should understand the mechanics of writing Spark and MapReduce jobs, and by the end of it you should feel comfortable actually implementing them.

A chapter on data mining and warehousing comes next, focusing on Hive, Hadoop's SQL-based query engine, along with its NoSQL database, HBase. This is followed by chapter exploring how to get data into a distributed system. There's a good description of how to use Sqoop for bulk loading, and Apache Flume for dealing with unstructured data such as logs.

Analytics with higher-level APIs is covered next, with a look at Apache Pig and Spark DataFrames API. There's a good chapter on machine learning and how to use Spark MTLib, and the book ends with a summary chapter called Doing Distributed Data Science in which the authors go through the whole lifecycle of distributed data science showing how it all fits together.

 

 

I liked this book; it gives a good introduction to the Hadoop ecosystem, concentrating on the analysis side and mainly ignoring the day-to-day administration.The descriptions are good, not too long-winded. The authors give pointers to places where you can read more, so you don't miss out where they give an overview rather than a detailed explanation. The Python examples are good, and used well to explain ideas. Perhaps the most useful part of the book is the final chapter where they show how to do an entire analytic workflow from start to finish. Well worth a read.

 

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Closure: The Definitive Guide

Author: Michael Bolin
Publisher: O'Reilly, 2010
Pages: 592
ISBN: 978-1449381875
Print: 1449381871
Kindle: B0046RERYI
Audience: Existing and potential users of Closure
Rating: 4
Reviewed by: Mike James 


Closure is Google's very strange JavaScript compiler - does this book succeed in demystify [ ... ]



Improving Agile Retrospectives

Author: Marc Loeffler
Publisher: Addison-Wesley
Pages: 270
ISBN: 978-0134678344
Print: 0134678346
Kindle: B0785W7PM6
Audience: Developers using Agile
Rating: 4
Reviewer: Kay Ewbank

 

The use of retrospectives in agile development is one of the key ideas behind the methodology, and this book aims [ ... ]


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Field Guide to Hadoop

Data Science and Big Data Analytics

Hadoop: The Definitive Guide (4th ed)

Hadoop Application Architectures

See also Reading Your Way Into Big Data

 

 

Last Updated ( Friday, 23 September 2016 )