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Big Data Fundamentals: Concepts, Drivers & Techniques (The Prentice Hall Service Technology Series From Thomas Erl)

“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 “ 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

File Size: 12531 KB

Print Length: 241 pages

Page Numbers Source ISBN: 0134291077

Simultaneous Device Usage: Up to 5 simultaneous devices, per publisher limits

Publisher: Prentice Hall; 1 edition (December 29, 2015)

Publication Date: December 29, 2015

Sold by:  Digital Services LLC

Language: English


Text-to-Speech: Enabled

X-Ray: Not Enabled

Word Wise: Not Enabled

Lending: Not Enabled

Enhanced Typesetting: Enabled

Best Sellers Rank: #479,547 Paid in Kindle Store (See Top 100 Paid in Kindle Store) #237 in Books > Computers & Technology > Databases & Big Data > Data Warehousing #408 in Books > Computers & Technology > Databases & Big Data > Data Mining #7658 in Kindle Store > Kindle eBooks > Computers & Technology

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.

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