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Big Data Visualization

Big Data Visualization

By : James D. Miller
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Big Data Visualization

Big Data Visualization

2 (1)
By: James D. Miller
Buy this Book

Overview of this book

Gain valuable insight into big data analytics with this book. Covering the tools you need to analyse data, together with IBM certified expert James Miller?s insight, this book is the key to data visualization success. ? Learn the tools & techniques to process big data for efficient data visualization ? Packed with insightful real-world use cases ? Addresses the difficulties faced by professionals in the field of big data analytics
Table of Contents (9 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction to Big Data Visualization
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1. Introduction to Big Data Visualization
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An explanation of data visualization
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Challenges of big data visualization
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Approaches to big data visualization
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Summary
2
2. Access, Speed, and Storage with Hadoop
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2. Access, Speed, and Storage with Hadoop
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About Hadoop
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Log files and Excel
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Hadoop and big data
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Example 1
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Example 2
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Summary
3
3. Understanding Your Data Using R
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3. Understanding Your Data Using R
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Definitions and explanations
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Adding context
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About R
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Example 1
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Digging in with R
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Example 2
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Summary
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4. Addressing Big Data Quality
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4. Addressing Big Data Quality
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Data quality categorized
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DataManager
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DataManager and big data
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Some examples
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More examples
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Summary
5
5. Displaying Results Using D3
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5. Displaying Results Using D3
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About D3
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D3 and big data
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Some basic examples
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More examples
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Summary
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6. Dashboards for Big Data - Tableau
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6. Dashboards for Big Data - Tableau
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About Tableau
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Tableau and big data
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Example 1 - Sales transactions
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Example 2
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Summary
7
7. Dealing with Outliers Using Python
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7. Dealing with Outliers Using Python
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About Python
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Python and big data
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Outliers
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Some basic examples
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More examples
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Summary
8
8. Big Data Operational Intelligence with Splunk
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8. Big Data Operational Intelligence with Splunk
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About Splunk
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Splunk visualization -  real-time log analysis
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Splunk visualization - deeper into the logs
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Summary

About Hadoop

Let's start out with an explanation of Hadoop that is generally circulated.

As per Apache Hadoop wikipedia.org, 2016:

"Hadoop is an open-source software "framework" for distributed storage and distributed processing (of very large datasets) on computer clusters built from commodity hardware."

The following is a visualization that may help understand the master-to-slave architecture used by Hadoop:

Hadoop uses an architecture called MapReduce. This is a design that designates a processor (in a cluster of processors) as the master, which controls distributing or mapping tasks to other slave processors to process your data, thus reducing the processing performed by the cluster of processors to a single output result. So, you can now see that the name mapped reduction or MapReduce (of processing tasks) makes sense.

Hadoop is able to take your data and split it up (or distribute it) over a number of computers that have space or resources available.

These computers need not be high...

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