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<p>Preface ix</p> <p>Acknowledgments xiii</p> <p><b>Chapter 1 What Is Big Data? 1</b></p> <p>The Arrival of Analytics 2</p> <p>Where Is the Value? 3</p> <p>More to Big Data Than Meets the Eye 5</p> <p>Dealing with the Nuances of Big Data 6</p> <p>An Open Source Brings Forth Tools 7</p> <p>Caution: Obstacles Ahead 8</p> <p><b>Chapter 2 Why Big Data Matters 11</b></p> <p>Big Data Reaches Deep 12</p> <p>Obstacles Remain 13</p> <p>Data Continue to Evolve 15</p> <p>Data and Data Analysis Are Getting More Complex 17</p> <p>The Future Is Now 18</p> <p><b>Chapter 3 Big Data and the Business Case 21</b></p> <p>Realizing Value 22</p> <p>The Case for Big Data 22</p> <p>The Rise of Big Data Options 25</p> <p>Beyond Hadoop 27</p> <p>With Choice Come Decisions 28</p> <p><b>Chapter 4 Building the Big Data Team 29</b></p> <p>The Data Scientist 29</p> <p>The Team Challenge 30</p> <p>Different Teams, Different Goals 31</p> <p>Don’t Forget the Data 32</p> <p>Challenges Remain 32</p> <p>Teams versus Culture 34</p> <p>Gauging Success 35</p> <p><b>Chapter 5 Big Data Sources .37</b></p> <p>Hunting for Data 38</p> <p>Setting the Goal 39</p> <p>Big Data Sources Growing 40</p> <p>Diving Deeper into Big Data Sources 42</p> <p>A Wealth of Public Information 43</p> <p>Getting Started with Big Data Acquisition 44</p> <p>Ongoing Growth, No End in Sight 46</p> <p><b>Chapter 6 The Nuts and Bolts of Big Data 47</b></p> <p>The Storage Dilemma 47</p> <p>Building a Platform 52</p> <p>Bringing Structure to Unstructured Data 57</p> <p>Processing Power 59</p> <p>Choosing among In-house, Outsourced, or Hybrid Approaches 61</p> <p><b>Chapter 7 Security, Compliance, Auditing, and Protection 63</b></p> <p>Pragmatic Steps to Securing Big Data 64</p> <p>Classifying Data 65</p> <p>Protecting Big Data Analytics 66</p> <p>Big Data and Compliance 67</p> <p>The Intellectual Property Challenge 72</p> <p><b>Chapter 8 The Evolution of Big Data 77</b></p> <p>Big Data: The Modern Era 80</p> <p>Today, Tomorrow, and the Next Day 84</p> <p>Changing Algorithms 90</p> <p><b>Chapter 9 Best Practices for Big Data Analytics 93</b></p> <p>Start Small with Big Data 94</p> <p>Thinking Big 95</p> <p>Avoiding Worst Practices 96</p> <p>Baby Steps 98</p> <p>The Value of Anomalies 101</p> <p>Expediency versus Accuracy 103</p> <p>In-Memory Processing 104</p> <p><b>Chapter 10 Bringing It All Together 111</b></p> <p>The Path to Big Data 112</p> <p>The Realities of Thinking Big Data 113</p> <p>Hands-on Big Data 115</p> <p>The Big Data Pipeline in Depth 116</p> <p>Big Data Visualization 121</p> <p>Big Data Privacy 122</p> <p><b>Appendix Supporting Data 125</b></p> <p>“The MapR Distribution for Apache Hadoop” 126</p> <p>“High Availability: No Single Points of Failure” 142</p> <p>About the Author 151</p> <p>Index 153</p>

<p><b>FRANK J. OHLHORST</b> 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.

<p><b>BIG DATA ANALYTICS</b> <p>TURNING BIG DATA INTO BIG MONEY <p>The promises offered by Big Data–driven decision making have been recognized broadly. Once the domain of only the heaviest of hitters—including Amazon, eBay, and Disney—Big Data analytics is now accessible to businesses of all sizes, across all industries. In Big Data Analytics, you'll learn how to extract intelligence and value out of your large data sets to grow your business, your bottom line, and your competitive edge. <p>Focusing on the business and financial value of Big Data analytics, technical guru Frank Ohlhorst shares his insights on this newly emerging field to demonstrate the importance of analytics and define the processes. In Big Data Analytics, Ohlhorst highlights the tangible and intangible values and discusses how to turn a business liability—such as large scale data storage, backup, and archiving—into actionable material that can be used to redefine markets, improve profits, and identify new business opportunities. <p>Beginning with the evolution of the technology as well as some of the basic concepts behind deriving value, Big Data Analytics explores: <ul> <li>The different types of data sources involved and why they are important to businesses seeking to find value in data sets</li> <li>Building a business case that spurs investment into technologies</li> <li>The skill sets needed to successfully leverage your data sets</li> <li>How to incorporate Big Data into a corporate culture</li> <li>Various public and private sources for data</li> <li>Storage, processing power, platforms, and the elements that make up a Big Data analytics system</li> <li>The tools and techniques that keep large data sources secure</li> <li>Potential pitfalls and how to avoid them</li> <li>How Big Data moves through the analysis process and is transformed into usable information that delivers value</li> </ul> <p>To become bigger, you must think bigger. Discover the value in your data sets and maximize your bottom line with Big Data Analytics.

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