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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
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
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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 R

We've dabbled a little bit in Chapter 2, Access, Speed, and Storage with Hadoop, with R programming, but in this chapter, we now formally introduce R as the tool to perform our data profiling exercises as well as adding perspectives (establish context) for data to be used in visualizations.

R is a language and environment easy to learn, very flexible in nature, and also very focused on statistical computing thus making it great for manipulating, cleaning, summarizing, producing probability statistics, and so on (as well as actually creating visualizations with your data), so it's a great choice for the exercises required for profiling, establishing context, and identifying additional perspectives.

In addition, here are a few more reasons to use R when profiling your big data:

  • R is used by a large number of academic statisticians, so it's a tool that is not going away.

  • R is pretty much platform independent, what you develop will run almost anywhere.

  • R has awesome help resources--just Google...

End of Section 6
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