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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 (9) Chapters

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

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...

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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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