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Big Data Visualization电子书

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作       者:James D. Miller

出  版  社:Packt Publishing

出版时间:2017-02-01

字       数:188.1万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization About This Book This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease It is rich with ample options and solid use cases for big data visualization, and is a must-have book for your shelf Improve your decision-making by visualizing your big data the right way Who This Book Is For This book is for data analysts or those with a basic knowledge of big data analysis who want to learn big data visualization in order to make their analysis more useful. You need sufficient knowledge of big data platform tools such as Hadoop and also some experience with programming languages such as R. This book will be great for those who are familiar with conventional data visualizations and now want to widen their horizon by exploring big data visualizations. What You Will Learn Understand how basic analytics is affected by big data Deep dive into effective and efficient ways of visualizing big data Get to know various approaches (using various technologies) to address the challenges of visualizing big data Comprehend the concepts and models used to visualize big data Know how to visualize big data in real time and for different use cases Understand how to integrate popular dashboard visualization tools such as Splunk and Tableau Get to know the value and process of integrating visual big data with BI tools such as Tableau Make sense of the visualization options for big data, based upon the best suited visualization techniques for big data In Detail When it comes to big data, regular data visualization tools with basic features become insufficient. This book covers the concepts and models used to visualize big data, with a focus on efficient visualizations. This book works around big data visualizations and the challenges around visualizing big data and address characteristic challenges of visualizing like speed in accessing, understanding/adding context to, improving the quality of the data, displaying results, outliers, and so on. We focus on the most popular libraries to execute the tasks of big data visualization and explore "big data oriented" tools such as Hadoop and Tableau. We will show you how data changes with different variables and for different use cases with step-through topics such as: importing data to something like Hadoop, basic analytics. The choice of visualizations depends on the most suited techniques for big data, and we will show you the various options for big data visualizations based upon industry-proven techniques. You will then learn how to integrate popular visualization tools with graphing databases to see how huge amounts of certain data. Finally, you will find out how to display the integration of visual big data with BI using Cognos BI. Style and approach With the help of insightful real-world use cases, we’ll tackle data in the world of big data. The scalability and hugeness of the data makes big data visualizations different from normal data visualizations, and this book addresses all the difficulties encountered by professionals while visualizing their big data.
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Big Data Visualization

Big Data Visualization

Credits

About the Author

About the Reviewer

www.PacktPub.com

Why subscribe?

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Introduction to Big Data Visualization

An explanation of data visualization

Conventional data visualization concepts

Training options

Challenges of big data visualization

Big data

Using Excel to gauge your data

Pushing big data higher

The 3Vs

Volume

Velocity

Variety

Categorization

Such are the 3Vs

Data quality

Dealing with outliers

Meaningful displays

Adding a fourth V

Visualization philosophies

More on variety

Velocity

Volume

All is not lost

Approaches to big data visualization

Access, speed, and storage

Entering Hadoop

Context

Quality

Displaying results

Not a new concept

Instant gratifications

Data-driven documents

Dashboards

Outliers

Investigation and adjudication

Operational intelligence

Summary

2. Access, Speed, and Storage with Hadoop

About Hadoop

What else but Hadoop?

IBM too!

Log files and Excel

An R scripting example

Points to consider

Hadoop and big data

Entering Hadoop

AWS for Hadoop projects

Example 1

Defining the environment

Getting started

Uploading the data

Manipulating the data

A specific example

Conclusion

Example 2

Sorting

Parsing the IP

Summary

3. Understanding Your Data Using R

Definitions and explanations

Comparisons

Contrasts

Tendencies

Dispersion

Adding context

About R

R and big data

Example 1

Digging in with R

Example 2

Definitions and explanations

No looping

Comparisons

Contrasts

Tendencies

Dispersion

Summary

4. Addressing Big Data Quality

Data quality categorized

DataManager

DataManager and big data

Some examples

Some reformatting

A little setup

Selecting nodes

Connecting the nodes

The work node

Adding the script code

Executing the scene

Other data quality exercises

What else is missing?

Status and relevance

Naming your nodes

More examples

Consistency

Reliability

Appropriateness

Accessibility

Other Output nodes

Summary

5. Displaying Results Using D3

About D3

D3 and big data

Some basic examples

Getting started with D3

A little down time

Visual transitions

Multiple donuts

More examples

Another twist on bar chart visualizations

One more example

Adopting the sample

Summary

6. Dashboards for Big Data - Tableau

About Tableau

Tableau and big data

Example 1 - Sales transactions

Adding more context

Wrangling the data

Moving on

A Tableau dashboard

Saving the workbook

Presenting our work

More tools

Example 2

What's the goal? - purpose and audience

Sales and spend

Sales v Spend and Spend as % of Sales Trend

Tables and indicators

All together now

Summary

7. Dealing with Outliers Using Python

About Python

Python and big data

Outliers

Options for outliers

Delete

Transform

Outliers identified

Some basic examples

Testing slot machines for profitability

Into the outliers

Handling excessive values

Establishing the value

Big data note

Setting outliers

Removing Specific Records

Redundancy and risk

Another point

If Type

Reused

Changing specific values

Setting the Age

Another note

Dropping fields entirely

More to drop

More examples

A themed population

A focused philosophy

Summary

8. Big Data Operational Intelligence with Splunk

About Splunk

Splunk and big data

Splunk visualization - real-time log analysis

IBM Cognos

Pointing Splunk

Setting rows and columns

Finishing with errors

Splunk and processing errors

Splunk visualization - deeper into the logs

New fields

Editing the dashboard

More about dashboards

Summary

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