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Big Data Visualization

Big Data Visualization

By : James D. Miller
2 (1)
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Big Data Visualization

Big Data Visualization

2 (1)
By: James D. Miller

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
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Introduction to Big Data Visualization
1. Introduction to Big Data Visualization
An explanation of data visualization
Challenges of big data visualization
Approaches to big data visualization
Summary
2
2. Access, Speed, and Storage with Hadoop
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2. Access, Speed, and Storage with Hadoop
About Hadoop
Log files and Excel
Hadoop and big data
Example 1
Example 2
Summary
3
3. Understanding Your Data Using R
3. Understanding Your Data Using R
Definitions and explanations
Adding context
About R
Example 1
Digging in with R
Example 2
Summary
4
4. Addressing Big Data Quality
4. Addressing Big Data Quality
Data quality categorized
DataManager
DataManager and big data
Some examples
More examples
Summary
5
5. Displaying Results Using D3
5. Displaying Results Using D3
About D3
D3 and big data
Some basic examples
More examples
Summary
6
6. Dashboards for Big Data - Tableau
6. Dashboards for Big Data - Tableau
About Tableau
Tableau and big data
Example 1 - Sales transactions
Example 2
Summary
7
7. Dealing with Outliers Using Python
7. Dealing with Outliers Using Python
About Python
Python and big data
Outliers
Some basic examples
More examples
Summary
8
8. Big Data Operational Intelligence with Splunk
8. Big Data Operational Intelligence with Splunk
About Splunk
Splunk visualization -  real-time log analysis
Splunk visualization - deeper into the logs
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:

About 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...

End of Section 2
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