Big Data Fundamentals: Concepts, Drivers & Techniques
- Prentice Hall/PearsonPTR
- ISBN: 0134291077
- Paperback, 218 pages
- Authors (in alphabetical order):
- Paul Buhler, PhD
- Thomas Erl
- Wajid Khattak
Table of Contents
- Part I: The Fundamentals of Big Data
- 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
- Part II: Storing and Analyzing Big Data
- 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
About this Book
Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies, and show how Big Data solution environment can be built and integrated to offer competitive advantages.
Topic areas covered include:
- Discovering Big Data's fundamental concepts and what makes it different from previous forms of data analysis and data science
- Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation
- Planning strategic, business-driven Big Data initiatives
- Addressing considerations such as data management, governance, and security
- Recognizing the 5 "V" characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value
- Clarifying Big Data's relationships with OLTP, OLAP, ETL, data warehouses, and data marts
- Working with Big Data in structured, unstructured, semi-structured, and metadata formats
- Increasing value by integrating Big Data resources with corporate performance monitoring
- Understanding how Big Data leverages distributed and parallel processing
- Using NoSQL and other technologies to meet Big Data's distinct data processing requirements
- Leveraging statistical approaches of quantitative and qualitative analysis
- Applying computational analysis methods, including machine learning
"Big Data Fundamentals offers the reader not a technical primer but a conceptual framework for understanding the increasingly critical notions underlying data-driven operations and the post-relational world that defines today's business environment. Experienced data scientists are in high-demand and rare supply, but for everyone else, read this book!"
- Nathaniel Palmer, BPM.com
"Finally, a Big Data book that bridges the gap! It contextualizes the complexity of big data solutions but remains relevant and accessible to non-technical decision makers. This text should be required reading for everyone in contemporary business."
- Peter Woodhull, CEO, Modus21
"Big Data Fundamentals: Concepts, Drivers & Techniques is the one book that clearly describes and links big data concepts to business utility. From drivers that shape strategy to techniques that deliver results, this is the book I've been seeking for my big data consultancy."
- Dr. Christopher Starr, PhD
"The Big Data Landscape is changing so fast. What is constant though is the need to fully grasp the fundamentals, concepts and techniques that will shape solutions to drive business value. This book distills the hype into a practical set of building blocks and establishes a solid framework to guide Big Data initiatives and includes engaging real-world scenarios and case studies that appeal to both business leaders and technical individuals."
"Simply, this is the best Big Data book on the market!"
- Sam Rostam, Cascadian IT Group Inc.
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