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“This text should be required reading for everyone in contemporary business.”
--Peter Woodhull, CEO, Modus21
“The one book that clearly describes and links Big Data concepts to business utility.”
--Dr. Christopher Starr, PhD
“Simply, this is the best Big Data book on the market!”
--Sam Rostam, Cascadian IT Group
“...one of the most contemporary approaches I’ve seen to Big Data fundamentals...”
--Joshua M. Davis, PhD
The Definitive Plain-English Guide to Big Data for Business and Technology Professionals
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 a Big Data solution environment can be built and integrated to offer competitive advantages.
- 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
- Sales Rank: #155309 in Books
- Published on: 2016-01-15
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .70" w x 6.90" l, .0 pounds
- Binding: Paperback
- 240 pages
About the Author
Thomas Erl is a top-selling IT author, founder of Arcitura Education and series editor of the Prentice Hall Service Technology Series from Thomas Erl. With more than 200,000 copies in print worldwide, his books have become international bestsellers and have been formally endorsed by senior members of major IT organizations, such as IBM, Microsoft, Oracle, Intel, Accenture, IEEE, HL7, MITRE, SAP, CISCO, HP and many others. As CEO of Arcitura Education Inc., Thomas has led the development of curricula for the internationally recognized Big Data Science Certified Professional (BDSCP), Cloud Certified Professional (CCP) and SOA Certified Professional (SOACP) accreditation programs, which have established a series of formal, vendor-neutral industry certifications obtained by thousands of IT professionals around the world. Thomas has toured more than 20 countries as a speaker and instructor. More than 100 articles and interviews by Thomas have been published in numerous publications, including The Wall Street Journal and CIO Magazine.
Wajid Khattak is a Big Data researcher and trainer at Arcitura Education Inc. His areas of interest include Big Data engineering and architecture, data science, machine learning, analytics and SOA. He has extensive .NET software development experience in the domains of business intelligence reporting solutions and GIS.
Wajid completed his MSc in Software Engineering and Security with distinction from Birmingham City University in 2008. Prior to that, in 2003, he earned his BSc (Hons) degree in Software Engineering from Birmingham City University with first-class recognition. He holds MCAD & MCTS (Microsoft), SOA Architect, Big Data Scientist, Big Data Engineer and Big Data Consultant (Arcitura) certifications.
Dr. Paul Buhler is a seasoned professional who has worked in commercial, government and academic environments. He is a respected researcher, practitioner and educator of service-oriented computing concepts, technologies and implementation methodologies. His work in XaaS naturally extends to cloud, Big Data and IoE areas. Dr. Buhler’s more recent work has been focused on closing the gap between business strategy and process execution by leveraging responsive design principles and goal-based execution.
As Chief Scientist at Modus21, Dr. Buhler is responsible for aligning corporate strategy with emerging trends in business architecture and process execution frameworks. He also holds an Affiliate Professorship at the College of Charleston, where he teaches both graduate and undergraduate computer science courses. Dr. Buhler earned his Ph.D. in Computer Engineering at the University of South Carolina. He also holds an MS degree in Computer Science from Johns Hopkins University and a BS in Computer Science from The Citadel.
Most helpful customer reviews
7 of 7 people found the following review helpful.
Decent Big Data Book Falls Flat In A Few Areas
By William P Ross
This book is divided into two parts with the first part introducing concepts about Big Data, and the second part discussing implementations of Big Data. I found the first part to use confusing wording, while the second part was written much better. I would give the first part 3-stars and the second part 5-stars if I was rating them individually.
The first part of the book (Chapters 1-4) introduces a lot of acronyms and words that were glossed over. A lot of sentences were written in overly complicated language. To give an example on page 36 the discussion is about Business Process Management:
"When BPM is combined with BPMSs that are intelligent, processes can be executed in a goal-driven manner. Goals are connected to process fragments that are dynamically chosen and assembled at run-time in alignment with the evaluation of the goals. When the combination of Big Data analytics results and goal-driven behavior are used together, process execution can become adaptive to the marketplace and responsive to environmental conditions."
I found wording like this to be bogged down in corporate mumbo-jumbo and had I difficulty understanding in a lot of places.
Chapter three felt particularly lazy to me. The exact same diagram that took up 3/4th's of the page was used 10 seperate times in chapter without any variation to the diagram (see the attached photo to get an idea). It shows a nine-step process, and for each step the diagram is shown without even highlighting the step we are on.
Luckily the second part redeems itself. MapReduce, different NoSQL databases, analytic techniques, and storage techniques were described well here. The second part of the book gave much clearer and more concrete examples. The writing was much better in the second part of the book. This led me to believe the parts were written mostly seperately by the authors.
Linking the chapters together is an insurance company called ETI. This is used as a case study at the end of each chapter. I felt the choice of an aging insurance company to be uninspiring for a big data solution. The analysis was oversimplified in these sections. For example, the authors might say something like the engineers at ETI are unfamiliar with CAP Theorem so they may need additional training. Or in one part, they said the company chose to go with a NoSQL database, but they do not mention what type of NoSQL database. I also noticed the writing in the second part to be better for these sections as well.
Overall this book does a decent job of conveying the concepts for big data. It is heavily geared towards a corporate environment as there is almost zero talk of implementating a Big Data solution on your own. I felt it covered the topic pretty well though, but would have like to see the authors discuss specific technologies more, rather than gearing the book towards getting certified in Big Data.
0 of 2 people found the following review helpful.
Good information
By A. Browner
Great book that is an easy read
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