Big Data Fundamentals: Concepts, Drivers & Techniques

Big Data Fundamentals: Concepts, Drivers & Techniques
by Paul Buhler, PhD
Thomas Erl
Wajid Khattak

Part I: The Fundamentals of Big Data

img
  • Chapter 1: Understanding Big Data
  • Chapter 2: Business Motivations and Drivers for Big Data Adoption
  • Chapter 3: Big Data Adoption and Planning Considerations .
  • Chapter 4: Enterprise Technologies and Big Data Business Intelligence

Chapter 1: Understanding Big Data (TOC)

Chapter 1: Understanding Big Data

This chapter 1 delivers insight into key concepts and terminology that define the very essence of Big Data and the promise it holds to deliver sophisticated business insights. The various characteristics that distinguish Big Data datasets are explained, as are definitions of the different types of data that can be subject to its analysis techniques.

Chapter 2: Business Motivations and Drivers for Big Data Adoption (TOC)

Chapter 2: Business Motivations and Drivers for Big Data Adoption

This chapter seeks to answer the question of why businesses should be motivated to adopt Big Data as a consequence of underlying shifts in the marketplace and business world. Big Data is not a technology related to business transformation; instead, it enables innovation within an enterprise on the condition that the enterprise acts upon its insights.

Chapter 3: Big Data Adoption and Planning Considerations . (TOC)

Chapter 3: Big Data Adoption and Planning Considerations .

This chapter shows that Big Data is not simply “business as usual,” and that the decision to adopt Big Data must take into account many business and technology considerations. This underscores the fact that Big Data opens an enterprise to external data influences that must be governed and managed. Likewise, the Big Data analytics lifecycle imposes distinct processing requirements.

Chapter 4: Enterprise Technologies and Big Data Business Intelligence (TOC)

Chapter 4: Enterprise Technologies and Big Data Business Intelligence

This chapter examines current approaches to enterprise data warehousing and business intelligence. It then expands this notion to show that Big Data storage and analysis resources can be used in conjunction with corporate performance monitoring tools to broaden the analytic capabilities of the enterprise and deepen the insights delivered by Business Intelligence.

Part II: Storing and Analyzing Big Data

img
  • Chapter 5: Big Data Storage Concepts
  • Chapter 6: Big Data Processing Concepts .
  • Chapter 7: Big Data Storage Technology
  • Chapter 8: Big Data Analysis Techniques
  • Appendix A: Case Study Conclusion

Chapter 5: Big Data Storage Concepts (TOC)

Chapter 5: Big Data Storage Concepts

This chapter explores key concepts related to the storage of Big Data datasets. These concepts inform the reader of how Big Data storage has radically different characteristics than the relational database technology common to traditional business information systems.

Chapter 6: Big Data Processing Concepts . (TOC)

Chapter 6: Big Data Processing Concepts .

This chapter provides insights into how Big Data datasets are processed by leveraging distributed and parallel processing capabilities. This is further illustrated with an examination of the MapReduce framework, which shows how it leverages a divide-and-conquer approach to efficiently process Big Data datasets.

Chapter 7: Big Data Storage Technology (TOC)

Chapter 7: Big Data Storage Technology

This chapter expands upon the storage topic, showing how the concepts from Chapter 5 are implemented with different flavors of NoSQL database technology. The requirements of batch and realtime processing modes are further explored from the perspective of on-disk and in-memory storage options.

Chapter 8: Big Data Analysis Techniques (TOC)

Chapter 8: Big Data Analysis Techniques

This chapter provides an introduction to a range of Big Data analysis techniques. The analysis of Big Data leverages statistical approaches for quantitative and qualitative analysis, whereas computational approaches are used for data mining and machine learning.

Appendix A: Case Study Conclusion (TOC)

Appendix A: Case Study Conclusion