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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

D3 and big data

First, let me say that you can easily bind or use your large datasets to common SVG objects using the functions available in the D3.js libraries.

The data can even be in a variety of formats, most commonly JSON, comma-separated values (CSV), or geoJSON, but, if required, JavaScript functions can be written to read other data format.

However, large isn't big in the sense of big data. Realistically, binding a CSV file of 500 records cannot be likened to binding it to a file of 500,000 records.

So, can D3 really help in the context of big data?

Since it is low-level, D3 may seem like a bad fit for big data visualization projects. The D3.js libraries just won't work with gigabytes of data, but once you perform some preprocessing on the data, D3 can help make sense of the results.

In fact, in each of the previous chapters of this book, Chapter 2, Access, Speed, and Storage with Hadoop, (where we loaded data into a Hadoop environment and then used Hive to manipulate that data into workable...

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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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