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  • Book Overview & Buying Big Data Visualization
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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
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
Lock Free Chapter
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
4
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
6
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

Chapter 4. Addressing Big Data Quality

In this chapter, we will talk about the categories of categorized data quality and the challenges big data brings to them. In addition, we will offer examples demonstrating concepts for effectively addressing these areas.

The chapter is organized into the following main sections:

  • Data quality categorized
  • DataManager
  • DataManager and big data
  • Some examples
  • More examples

To make programming a bit easier, programming languages categorize data into types or a datatype. These categories of data are a defined kind or a set of possible values allowed by the type and allow progress to be made or, specifically, solutions to be crafted.

The same concept may be applied to the challenge of data quality. By understanding the categories of data quality, it makes it easier (while using an appropriate tool choice) to identify and address issues with the quality of your big data.

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Tech Concepts
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Programming languages
73
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Big Data Visualization
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