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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

2. Access, Speed, and Storage with Hadoop FREE CHAPTER

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

About Hadoop

Let's start out with an explanation of Hadoop that is generally circulated.

As per Apache Hadoop wikipedia.org, 2016:

"Hadoop is an open-source software "framework" for distributed storage and distributed processing (of very large datasets) on computer clusters built from commodity hardware."

The following is a visualization that may help understand the master-to-slave architecture used by Hadoop:

Hadoop uses an architecture called MapReduce. This is a design that designates a processor (in a cluster of processors) as the master, which controls distributing or mapping tasks to other slave processors to process your data, thus reducing the processing performed by the cluster of processors to a single output result. So, you can now see that the name mapped reduction or MapReduce (of processing tasks) makes sense.

Hadoop is able to take your data and split it up (or distribute it) over a number of computers that have space or resources available.

These computers need not be high...

You have been reading a chapter from

Big Data Visualization

Published in: Feb 2017

Publisher: Packt

ISBN-13: 9781785281945

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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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