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More About This Title Big Data Analytics: Turning Big Data into Big Money
English
Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities.
- Reveals big data analytics as the next wave for businesses looking for competitive advantage
- Takes an in-depth look at the financial value of big data analytics
- Offers tools and best practices for working with big data
Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.
English
FRANK J. OHLHORST is an award-winning technology journalist, professional speaker, and IT business consultant with over twenty-five years of experience in the technology arena. He has written for several leading technology publications, speaks at many industry conferences, and has several industry certifications.
English
Preface ix
Acknowledgments xiii
Chapter 1 What Is Big Data? 1
The Arrival of Analytics 2
Where Is the Value? 3
More to Big Data Than Meets the Eye 5
Dealing with the Nuances of Big Data 6
An Open Source Brings Forth Tools 7
Caution: Obstacles Ahead 8
Chapter 2 Why Big Data Matters 11
Big Data Reaches Deep 12
Obstacles Remain 13
Data Continue to Evolve 15
Data and Data Analysis Are Getting More Complex 17
The Future Is Now 18
Chapter 3 Big Data and the Business Case 21
Realizing Value 22
The Case for Big Data 22
The Rise of Big Data Options 25
Beyond Hadoop 27
With Choice Come Decisions 28
Chapter 4 Building the Big Data Team 29
The Data Scientist 29
The Team Challenge 30
Different Teams, Different Goals 31
Don’t Forget the Data 32
Challenges Remain 32
Teams versus Culture 34
Gauging Success 35
Chapter 5 Big Data Sources .37
Hunting for Data 38
Setting the Goal 39
Big Data Sources Growing 40
Diving Deeper into Big Data Sources 42
A Wealth of Public Information 43
Getting Started with Big Data Acquisition 44
Ongoing Growth, No End in Sight 46
Chapter 6 The Nuts and Bolts of Big Data 47
The Storage Dilemma 47
Building a Platform 52
Bringing Structure to Unstructured Data 57
Processing Power 59
Choosing among In-house, Outsourced, or Hybrid Approaches 61
Chapter 7 Security, Compliance, Auditing, and Protection 63
Pragmatic Steps to Securing Big Data 64
Classifying Data 65
Protecting Big Data Analytics 66
Big Data and Compliance 67
The Intellectual Property Challenge 72
Chapter 8 The Evolution of Big Data 77
Big Data: The Modern Era 80
Today, Tomorrow, and the Next Day 84
Changing Algorithms 90
Chapter 9 Best Practices for Big Data Analytics 93
Start Small with Big Data 94
Thinking Big 95
Avoiding Worst Practices 96
Baby Steps 98
The Value of Anomalies 101
Expediency versus Accuracy 103
In-Memory Processing 104
Chapter 10 Bringing It All Together 111
The Path to Big Data 112
The Realities of Thinking Big Data 113
Hands-on Big Data 115
The Big Data Pipeline in Depth 116
Big Data Visualization 121
Big Data Privacy 122
Appendix Supporting Data 125
“The MapR Distribution for Apache Hadoop” 126
“High Availability: No Single Points of Failure” 142
About the Author 151
Index 153
