Big Data Fundamentals: Concepts, Drivers & Techniques (The Pearson Service Technology Series from Thomas Erl)

“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 offers a practical, no-nonsense introduction to Big Data. Best-selling IT creator Thomas Erl and his crew clearly clarify key Big Data ideas, idea and terminology, in addition to elementary applied sciences and strategies. All protection is supported with case examine examples and quite a few easy diagrams.

The authors start by explaining how Big Data can propel a company ahead by fixing a spectrum of beforehand intractable enterprise issues. Next, they demystify key evaluation strategies and applied sciences and present how a Big Data answer atmosphere will be constructed and built-in to supply aggressive benefits.

• Discovering Big Data’s elementary ideas and what makes it totally different from earlier types of knowledge evaluation and knowledge science
• Understanding the enterprise motivations and drivers behind Big Data adoption, from operational enhancements via innovation
• Planning strategic, business-driven Big Data initiatives
• Addressing concerns similar to knowledge administration, governance, and safety
• Recognizing the 5 “V” traits of datasets in Big Data environments: quantity, velocity, selection, veracity, and worth
• Clarifying Big Data’s relationships with OLTP, OLAP, ETL, knowledge warehouses, and knowledge marts
• Working with Big Data in structured, unstructured, semi-structured, and metadata codecs
• Increasing worth by integrating Big Data assets with company efficiency monitoring
• Understanding how Big Data leverages distributed and parallel processing
• Using NoSQL and different applied sciences to satisfy Big Data’s distinct knowledge processing necessities
• Leveraging statistical approaches of quantitative and qualitative evaluation
• Applying computational evaluation strategies, together with machine studying