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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

Hadoop and big data

In this section, we'll consider why Hadoop is actually a very good choice for storing and accessing big data.

Imagine you want to process data, a lot of data. In our previous example, we considered the scenario where machine generated web logging files are being produced and we want to leverage information within those files to perform some analytics and produce some (hopefully) compelling data visualizations.

Using R worked here, but if we extend the scenario with the idea that we will continue to receive web log files over time and the size of those files will increase, R might not be a feasible answer.

Entering Hadoop

Hadoop (as the product documentation says) is not your average database. In fact, Hadoop can store all kinds of data from many servers and websites and corporate vaults--as much as you might need or want to gather. In addition, Hadoop spreads your work across hundreds or thousands of processors and storage drives working in parallel all at the same...

End of Section 4
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