You are previewing Big Data Analytics: Turning Big Data into Big Money.

Big Data Analytics: Turning Big Data into Big Money

Cover of Big Data Analytics: Turning Big Data into Big Money by Frank J. Ohlhorst Published by John Wiley & Sons
  1. Cover
  2. Contents
  3. Title
  4. Copyright
  5. Preface
  6. Acknowledgments
  7. 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
    4. Dealing with the Nuances of Big Data
    5. An Open Source Brings Forth Tools
    6. Caution: Obstacles Ahead
  8. Chapter 2: Why Big Data Matters
    1. Big Data Reaches Deep
    2. Obstacles Remain
    3. Data Continue to Evolve
    4. Data and Data Analysis are Getting More Complex
    5. The Future is Now
  9. Chapter 3: Big Data and the Business Case
    1. Realizing Value
    2. The Case for Big Data
    3. The Rise of Big Data Options
    4. Beyond Hadoop
    5. With Choice Come Decisions
  10. Chapter 4: Building the Big Data Team
    1. The Data Scientist
    2. The Team Challenge
    3. Different Teams, Different Goals
    4. Don’t Forget the Data
    5. Challenges Remain
    6. Teams versus Culture
    7. Gauging Success
  11. Chapter 5: Big Data Sources
    1. Hunting for Data
    2. Setting the Goal
    3. Big Data Sources Growing
    4. Diving Deeper into Big Data Sources
    5. A Wealth of Public Information
    6. Getting Started with Big Data Acquisition
    7. Ongoing Growth, No End in Sight
  12. Chapter 6: The Nuts and Bolts of Big Data
    1. The Storage Dilemma
    2. Building a Platform
    3. Bringing Structure to Unstructured Data
    4. Processing Power
    5. Choosing among In-house, Outsourced, or Hybrid Approaches
  13. Chapter 7: Security, Compliance, Auditing, and Protection
    1. Pragmatic Steps to Securing Big Data
    2. Classifying Data
    3. Protecting Big Data Analytics
    4. Big Data and Compliance
    5. The Intellectual Property Challenge
  14. Chapter 8: The Evolution of Big Data
    1. Big Data: The Modern Era
    2. Today, Tomorrow, and the Next Day
    3. Changing Algorithms
  15. Chapter 9: Best Practices for Big Data Analytics
    1. Start Small with Big Data
    2. Thinking Big
    3. Avoiding Worst Practices
    4. Baby Steps
    5. The Value of Anomalies
    6. Expediency versus Accuracy
    7. In-Memory Processing
  16. Chapter 10: Bringing it All Together
    1. The Path to Big Data
    2. The Realities of Thinking Big Data
    3. Hands-on Big Data
    4. The Big Data Pipeline in Depth
    5. Big Data Visualization
    6. Big Data Privacy
  17. Appendix: Supporting Data
    1. “The MapR Distribution for Apache Hadoop”
    2. “High Availability: No Single Points of Failure”
  18. About the Author
  19. Index
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Chapter 6

The Nuts and Bolts of Big Data

Assembling a Big Data solution is sort of like putting together an erector set. There are various pieces and elements that must be put together in the proper fashion to make sure everything works adequately, and there are almost endless combinations of configurations that can be made with the components at hand.

With Big Data, the components include platform pieces, servers, virtualization solutions, storage arrays, applications, sensors, and routing equipment. The right pieces must be picked and integrated in a fashion that offers the best performance, high efficiency, affordability, ease of management and use, and scalability.

THE STORAGE DILEMMA

Big Data consists of data sets that are too large to be acquired, handled, analyzed, or stored in an appropriate time frame using the traditional infrastructures. Big is a term relative to the size of the organization and, more important, to the scope of the IT infrastructure that’s in place. The scale of Big Data directly affects the storage platform that must be put in place, and those deploying storage solutions have to understand that Big Data uses storage resources differently than the typical enterprise application does.

These factors can make provisioning storage a complex endeavor, especially when one considers that Big Data also includes analysis; this is driven by the expectation that there will be value in all of the information a business is accumulating and a way to draw that value ...

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