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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
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
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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

Summary

In this chapter, we started by introducing Trifacta Wrangler as a means to profile and manipulate your raw big data into a format that can be easily consumed and visualized. In addition, we explored the concept of using a secondary data source to provide context to a primary data source.

Next, we presented Tableau as a tool to consume prepared data and create valuable visualizations as individual components of interactive dashboards.

In the next chapter, we will cover outliers and provide working example solutions to deal with outliers and other data anomalies using Python.

End of Chapter 6
Next Chapter
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