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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

Summary

In this chapter, we started by introducing Trifacta Wrangler as a means to profile and manipulate your raw big data into a format that can be easily consumed and visualized. In addition, we explored the concept of using a secondary data source to provide context to a primary data source.

Next, we presented Tableau as a tool to consume prepared data and create valuable visualizations as individual components of interactive dashboards.

In the next chapter, we will cover outliers and provide working example solutions to deal with outliers and other data anomalies using Python.

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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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