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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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Afterword
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End User License Agreement
Index
A
Advanced analytics
banking
cognitive
government
life cycle
in-database
in-memory
model development
model deployment
predictive analytics
prescriptive analytics
Analysis
analytical data set (ADS)
e-commerce
massively parallel processing
processes
sandbox
scalability
scoring
Analytics
traditional approach
Analytical Data Set
Analytic professionals
data modelers
data scientist
scoring officer
statisticians
Analytics technology
graphical user interface
in-database
in-memory
model development
model deployment
open source
predictive analytics
prescriptive analytics
visualization
Automated prescriptive analytics
B
Banking,
Best practices
hadoop
Big data
combine with traditional data
variety
velocity
volume
Big data sources
Business analysts
Business
Use cases
C
Centralized
Churn
Cleansed data,
Clickstream data
Cloud computing
Collaborative data architecture
Competitive advantage
Customer behavior
Cyber
D
Data analysis
Data exploration
Data preparation
Data quality
Data scientist
Data storage,
Data warehouse
Digital data
E
E-commerce
Economics
Enterprise data warehouse
Extract, transform and load (ETL) process
F
Financial
Foundation
Future of data management
Future of analytics
G
Governance
Government
Graphical user interface
H
Hackers
Hadoop
Hybrid cloud
HDFS
I
In-database
In-memory
In-database data quality
Innovative
Internet
Internet of things (IoT)
Investment
in-database
in-memory
collaborative data architecture
M
MapReduce,
Massively parallel processing (MPP)
Model development
Model deployment
N
Need for
In-database
In-memory
O
Open source technology
P
Performance
Predictive analytics
Prescriptive analytics
Private clouds
Public clouds
Production environment
R
Real-time
Relational database
Retail
Risk
S
Sandbox
Scalability
Security
Semi-structured data
Sensor data
Services
CaaS
DBaaS
DRaaS
IaaS
MaaS
PaaS
SaaS
XaaS
Social media
Success stories
T
Telecommunication
Traditional data
Transportation
U
Use cases
V
Variety
Velocity
Volume
Vision
Visualization
W
Web data
Web logs
Workload
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