Frank J. Ohlhorst

Big Data Analytics (SAS)

• Gebundenes Buch

Produktbeschreibung

Unique insights to implement big data analytics and reap big returns to your bottom line

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.

Produktdetails

• SAS Institute Inc .
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 176
• Erscheinungstermin: 28. November 2012
• Englisch
• Abmessung: 235mm x 157mm x 14mm
• Gewicht: 351g
• ISBN-13: 9781118147597
• ISBN-10: 1118147596
• Artikelnr.: 34448261

Autorenporträt

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.

Inhaltsangabe

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