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by Bill Franks, James Taylor, Tho H. Nguyen
Leaders and Innovators
Foreword
Acknowledgments
About the Author
Introduction
Why You Should Read This Book
Let's Start with Definitions
Industry Trends and Challenges
Who Should Read This Book?
How to Read This Book
Let Your Journey Begin
Endnotes
Chapter 1: The Analytical Data Life Cycle
Stage 1: Data Exploration
Stage 2: Data Preparation
Stage 3: Model Development
Stage 4: Model Deployment
End-to-End Process
Chapter 2: In-Database Processing
Background
Traditional Approach
In-Database Approach
The Need for In-Database Analytics
Success Stories and Use Cases
In-Database Data Quality
Investment for In-Database Processing
Endnotes
Chapter 3: In-Memory Analytics
Background
Traditional Approach
In-Memory Analytics Approach
The Need for In-Memory Analytics
Success Stories and Use Cases
Investment for In-Memory Analytics
Chapter 4: Hadoop
Background
Hadoop in the Big Data Environment
Use Cases for Hadoop
Hadoop Architecture
Best Practices
Benefits of Hadoop
Use Cases and Success Stories
A Collection of Use Cases
Endnote
Chapter 5: Bringing It All Together
Background
Collaborative Data Architecture
Scenarios for the Collaborative Data Architecture
How In-Database, In-Memory, and Hadoop Are Complementary in a Collaborative Data Architecture
Use Cases and Customer Success Stories
Investment and Costs
Endnotes
Chapter 6: Final Thoughts and Conclusion
Five Focus Areas
Cloud Computing
Security: Cyber, Data Breach
Automating Prescriptive Analytics: Iot, Events, and Data Streams
Cognitive Analytics
Anything as a Service (XaaS)
Conclusion
Afterword
Index
End User License Agreement
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Wiley & SAS Business Series
Table of Contents
Foreword
Acknowledgments
About the Author
Introduction
Why You Should Read This Book
Let's Start with Definitions
Industry Trends and Challenges
Who Should Read This Book?
How to Read This Book
Let Your Journey Begin
Endnotes
Chapter 1: The Analytical Data Life Cycle
Stage 1: Data Exploration
Stage 2: Data Preparation
Stage 3: Model Development
Stage 4: Model Deployment
End-to-End Process
Chapter 2: In-Database Processing
Background
Traditional Approach
In-Database Approach
The Need for In-Database Analytics
Success Stories and Use Cases
In-Database Data Quality
Investment for In-Database Processing
Endnotes
Chapter 3: In-Memory Analytics
Background
Traditional Approach
In-Memory Analytics Approach
The Need for In-Memory Analytics
Success Stories and Use Cases
Investment for In-Memory Analytics
Chapter 4: Hadoop
Background
Hadoop in the Big Data Environment
Use Cases for Hadoop
Hadoop Architecture
Best Practices
Benefits of Hadoop
Use Cases and Success Stories
A Collection of Use Cases
Endnote
Chapter 5: Bringing It All Together
Background
Collaborative Data Architecture
Scenarios for the Collaborative Data Architecture
How In-Database, In-Memory, and Hadoop Are Complementary in a Collaborative Data Architecture
Use Cases and Customer Success Stories
Investment and Costs
Endnotes
Chapter 6: Final Thoughts and Conclusion
Five Focus Areas
Cloud Computing
Security: Cyber, Data Breach
Automating Prescriptive Analytics: Iot, Events, and Data Streams
Cognitive Analytics
Anything as a Service (XaaS)
Conclusion
Afterword
Index
End User License Agreement
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Guide
Cover
Table of Contents
Begin Reading
List of Illustrations
Chapter 1: The Analytical Data Life Cycle
Figure 1.1 Analytical data life cycle
Figure 1.2 Technologies for the analytical data life cycle
Chapter 2: In-Database Processing
Figure 2.1 Traditional approach to analytics
Figure 2.2 In-database approach to analytics
Figure 2.3 Data and analytic ecosystem
Figure 2.4 Traditional approach
Figure 2.5 In-database process
Chapter 3: In-Memory Analytics
Figure 3.1 In-memory analytics
Figure 3.2 CFO mandate for the project
Figure 3.3 From silos model to a functional, integrated architecture
Figure 3.4 Integrating data and analytics
Figure 3.5 In-memory analytics process
Figure 3.6 Manual lookup and paperwork
Figure 3.7 Distribution of information
Chapter 4: Hadoop
Figure 4.1 Hadoop, HDFS, and MapReduce
Figure 4.2 Traditional architecture
Figure 4.3 Hadoop architecture
Chapter 5: Bringing It All Together
Figure 5.1 Collaborative data architecture
Figure 5.2 Hadoop as a staging warehouse for your structured data
Figure 5.3 Hadoop as a data lake
Figure 5.4 Hadoop for data exploration and discovery
Figure 5.5 In-database processing
Figure 5.6 Hadoop for data exploration
Figure 5.7 No Hadoop
Figure 5.8 Integrating Hadoop, in-database, and in-memory
Figure 5.9 Retail traditional process for analytics
Figure 5.10 Integrating in-database and in-memory
Figure 5.11 Public administration architecture for fraud
Figure 5.12 Integrating Hadoop, in-database, and in-memory
Chapter 6: Final Thoughts and Conclusion
Figure 6.1 Top five focus areas
Figure 6.2 Cloud computing
Figure 6.3 Typical cloud computing services
Figure 6.4 Cyber-attacks by industry
Figure 6.5 Prescriptive analytics
Figure 6.6 Internet of Things connectivity
Figure 6.7 Cognitive, prescriptive, predictive, and descriptive analytics
Figure 6.8 Types of disaster incidents
List of Tables
Introduction
Table 1 Outline of the Chapters
Chapter 2: In-Database Processing
Table 2.1 Traditional Run Times at Different Process
Table 2.2 In-Database Run Times at Different Process
Table 2.3 Benefits of In-Database Processing
Table 2.4 Variations of Title
Table 2.5 Duplicate Records
Chapter 4: Hadoop
Table 4.1 Big Data Sources
Chapter 6: Final Thoughts and Conclusion
Table 6.1 Causes of Data Breaches
Table 6.2 Different Types of Analytics (Descriptive, Predictive, and Prescriptive)
Table 6.3 Industry Uses of IoT
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