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Big Data Visualization

You're reading from Big Data Visualization Bring scalability and dynamics to your Big Data visualization

Product type Paperback

Published in Feb 2017

Publisher Packt

ISBN-13 9781785281945

Length 304 pages

Edition 1st Edition

Languages

R

Tools

D3

Concepts

Big Data

Author (1):

James D. Miller

View More author details

Table of Contents (15) Chapters

Big Data Visualization

Credits

About the Author

About the Reviewer

www.PacktPub.com

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Preface

1. Introduction to Big Data Visualization FREE CHAPTER

2. Access, Speed, and Storage with Hadoop

3. Understanding Your Data Using R

4. Addressing Big Data Quality

5. Displaying Results Using D3

6. Dashboards for Big Data - Tableau

7. Dealing with Outliers Using Python

8. Big Data Operational Intelligence with Splunk

Summary

In this chapter, we provided the reader with a high-level definition of Hadoop, including some fun Hadoop FAQs. We mentioned that simply reaching MS Excel limitations doesn't mean that you are actually dealing with big data and used simple examples of R programming scripts to actually manipulate and visualize that same data that would not load in Excel to prove that point.

We then introduced the Amazon AWS environment as a simple, affordable, yet robust solution for leveraging the technology and power of Hadoop. We stepped through the process configuring that environment for our use, uploading our multiple web log files to it, and then used Hive and its query language (HiveQL) to access and manipulate that data to accomplish the same objectives as we did with our R programming scripts.

Finally, we offered some alternative HiveQL working examples using the same uploaded web log data.

In the next chapter, we will discuss the importance of understanding the data you are working with, the...

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Authors (1)

James D. Miller

James D. Miller

James D. Miller is an IBM certified expert, Master Consultant, Application/System Architect with +35 years of applications & system design/development experience across multiple platforms, technologies and data formats, including Big Data. His experience includes IBM Planning Analytics, BI, Web architecture & design, systems analysis, GUI design & testing, Data modeling, design, and development of OLAP, Client/Server, Web & Mainframe applications and systems utilizing: Planning Analytics Workspace (PAW), IBM Watson Analytics, Cognos BI & TM1, Framework Manager, dynaSight/ArcPlan, ASP, DHTML, XML, MS Visual Basic, VBA, PERL, R, SPLUNK, MS SQL Server, ORACLE, etc. He has authored numerous books, including Implementing Splunk - Second Edition; Mastering Splunk; Hands-On Machine Learning with IBM Watson; IBM Watson Projects; Statistics for Data Science; Mastering Predictive Analytics with R - Second Edition and others. Project areas include those with Data Analytics, Planning Analytics, and FOPM projects, holding various roles from architect, developer, technical and project leader.

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