Big data analytics : turning big data into big money / Frank Ohlhorst.

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Bibliographic Details
Main Author: Ohlhorst, Frank, 1964-
Language:English
Published: Hoboken, N.J. : Wiley, [2013], ©2013.
Series:Wiley and SAS business series.
Subjects:
Business intelligence.
Data mining.
Physical Description:xiv, 160 pages : illustrations ; 24 cm.
Format: Book

MARC

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245 1 0 |a Big data analytics :  |b turning big data into big money /  |c Frank Ohlhorst. 
260 |a Hoboken, N.J. :  |b Wiley,  |c [2013], ©2013. 
300 |a xiv, 160 pages :  |b illustrations ;  |c 24 cm. 
336 |a text  |b txt  |2 rdacontent 
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500 |a Includes index. 
505 0 0 |g Chapter 1  |t What Is Big Data?  |g 1 --  |t The Arrival of Analytics  |g 2 --  |t Where Is the Value?  |g 3 --  |t More to Big Data Than Meets the Eye  |g 5 --  |t Dealing with the Nuances of Big Data  |g 6 --  |t An Open Source Brings Forth Tools  |g 7 --  |t Caution: Obstacles Ahead  |g 8 --  |g Chapter 2  |t Why Big Data Matters  |g 11 --  |t Big Data Reaches Deep  |g 12 --  |t Obstacles Remain  |g 13 --  |t Data Continue to Evolve  |g 15 --  |t Data and Data Analysis Are Getting More Complex  |g 17 --  |t The Future Is Now  |g 18 --  |g Chapter 3  |t Big Data and the Business Case  |g 21 --  |t Realizing Value  |g 22 --  |t The Case for Big Data  |g 22 --  |t The Rise of Big Data Options  |g 25 --  |t Beyond Hadoop  |g 27 --  |t With Choice Come Decisions  |g 28 --  |g Chapter 4  |t Building the Big Data Team  |g 29 --  |t The Data Scientist  |g 29 --  |t The Team Challenge  |g 30 --  |t Different Teams, Different Goals  |g 31 --  |t Don't Forget the Data  |g 32 --  |t Challenges Remain  |g 32 --  |t Teams versus Culture  |g 34 --  |t Gauging Success  |g 35 --  |g Chapter 5  |t Big Data Sources  |g 37 --  |t Hunting for Data  |g 38 --  |t Setting the Goal  |g 39 --  |t Big Data Sources Growing  |g 40 --  |t Diving Deeper into Big Data Sources  |g 42 --  |t A Wealth of Public Information  |g 43 --  |t Getting Started with Big Data Acquisition  |g 44 --  |t Ongoing Growth, No End in Sight  |g 46 --  |g Chapter 6  |t The Nuts and Bolts of Big Data  |g 47 --  |t The Storage Dilemma  |g 47 --  |t Building a Platform  |g 52 --  |t Bringing Structure to Unstructured Data  |g 57 --  |t Processing Power  |g 59 --  |t Choosing among In-house, Outsourced, or Hybrid Approaches  |g 61 --  |g Chapter 7  |t Security, Compliance, Auditing, and Protection  |g 63 --  |t Pragmatic Steps to Securing Big Data  |g 64 --  |t Classifying Data  |g 65 --  |t Protecting Big Data Analytics  |g 66 --  |t Big Data and Compliance  |g 67 --  |t The Intellectual Property Challenge  |g 72 --  |g Chapter 8  |t The Evolution of Big Data  |g 77 --  |t Big Data: The Modem Era  |g 80 --  |t Today, Tomorrow, and the Next Day  |g 84 --  |t Changing Algorithms  |g 90 --  |g Chapter 9  |t Best Practices for Big Data Analytics  |g 93 --  |t Start Small with Big Data  |g 94 --  |t Thinking Big  |g 95 --  |t Avoiding Worst Practices  |g 96 --  |t Baby Steps  |g 98 --  |t The Value of Anomalies  |g 101 --  |t Expediency versus Accuracy  |g 103 --  |t In-Memory Processing  |g 104 --  |g Chapter 10  |t Bringing It All Together  |g 111 --  |t The Path to Big Data  |g 112 --  |t The Realities of Thinking Big Data  |g 113 --  |t Hands-on Big Data  |g 115 --  |t The Big Data Pipeline in Depth  |g 116 --  |t Big Data Visualization  |g 121 --  |t Big Data Privacy  |g 122. 
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