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Hadoop Blueprints

Hadoop Blueprints

By : Sudheesh Narayan, Anurag Shrivastava, Deshpande
5 (1)
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Hadoop Blueprints

Hadoop Blueprints

5 (1)
By: Sudheesh Narayan, Anurag Shrivastava, Deshpande

Overview of this book

If you have a basic understanding of Hadoop and want to put your knowledge to use to build fantastic Big Data solutions for business, then this book is for you. Build six real-life, end-to-end solutions using the tools in the Hadoop ecosystem, and take your knowledge of Hadoop to the next level. Start off by understanding various business problems which can be solved using Hadoop. You will also get acquainted with the common architectural patterns which are used to build Hadoop-based solutions. Build a 360-degree view of the customer by working with different types of data, and build an efficient fraud detection system for a financial institution. You will also develop a system in Hadoop to improve the effectiveness of marketing campaigns. Build a churn detection system for a telecom company, develop an Internet of Things (IoT) system to monitor the environment in a factory, and build a data lake – all making use of the concepts and techniques mentioned in this book. The book covers other technologies and frameworks like Apache Spark, Hive, Sqoop, and more, and how they can be used in conjunction with Hadoop. You will be able to try out the solutions explained in the book and use the knowledge gained to extend them further in your own problem space.
Table of Contents (9 chapters)
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Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "You can also run the transmodel.py program using the Python command-line interpreter pyspark."

A block of code is set as follows:

#!/bin/bash 
while [ true ] 
do 
echo 1 2 $RANDOM  
sleep 1 
done

Any command-line input or output is written as follows:

>>> from pyspark.mllib.clustering import KMeans, KMeansModel 
>>> from numpy import array

New terms and important words are shown in bold.

Note

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