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

Chapter 9

Best Practices for Big Data Analytics

Like any other technology or process, there obviously are best practices that can be applied to the problems of Big Data. In most cases, best practices usually arise from years of testing and measuring results, giving them a solid foundation to build on. However, Big Data, as it is applied today, is relatively new, short circuiting the tried-and-true methodology used in the past to derive best practices. Nevertheless, best practices are presenting themselves at a fairly accelerated rate, which means that we can still learn from the mistakes and successes of others to define what works best and what doesn’t.

The evolutionary aspect of Big Data tends to affect best practices, so what may be best today may not necessarily be best tomorrow. That said, there are still some core proven techniques that can be applied to Big Data analytics and that should withstand the test of time. With new terms, new skill sets, new products, and new providers, the world of Big Data analytics can seem unfamiliar, but tried-and-true data management best practices do hold up well in this still emerging discipline.

As with any business intelligence (BI) and/or data warehouse initiative, it is critical to have a clear understanding of an organization’s data management requirements and a well-defined strategy before venturing too far down the Big Data analytics path. Big Data analytics is widely hyped, and companies in all sectors are being flooded with new ...

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