Read e-book online Big Data Fundamentals: Concepts, Drivers & Techniques PDF

By Thomas Erl, Wajid Khattak, Paul Buhler

ISBN-10: 0134291077

ISBN-13: 9780134291079

“This textual content can be required analyzing for everybody in modern business.”
--Peter Woodhull, CEO, Modus21

“The one e-book that sincerely describes and hyperlinks tremendous information suggestions to enterprise utility.”
--Dr. Christopher Starr, PhD

“Simply, this is often the easiest large facts booklet at the market!”
--Sam Rostam, Cascadian IT Group

“ of the main modern methods I’ve obvious to important information fundamentals...”
--Joshua M. Davis, PhD

The Definitive Plain-English consultant to important facts for company and know-how execs

Big facts basics provides a realistic, no-nonsense creation to special facts. Best-selling IT writer Thomas Erl and his crew sincerely clarify key great info strategies, concept and terminology, in addition to primary applied sciences and strategies. All insurance is supported with case examine examples and diverse easy diagrams.

The authors commence through explaining how monstrous info can propel a firm ahead by means of fixing a spectrum of formerly intractable company difficulties. subsequent, they demystify key research innovations and applied sciences and convey how an enormous facts answer surroundings may be equipped and built-in to provide aggressive advantages.

  • Discovering significant Data’s primary techniques and what makes it diverse from prior kinds of information research and knowledge science
  • Understanding the enterprise motivations and drivers at the back of sizeable info adoption, from operational advancements via innovation
  • Planning strategic, business-driven massive info initiatives
  • Addressing issues equivalent to facts administration, governance, and security
  • Recognizing the five “V” features of datasets in titanic information environments: quantity, pace, sort, veracity, and value
  • Clarifying large Data’s relationships with OLTP, OLAP, ETL, information warehouses, and knowledge marts
  • Working with giant information in based, unstructured, semi-structured, and metadata formats
  • Increasing price through integrating titanic info assets with company functionality monitoring
  • Understanding how colossal info leverages dispensed and parallel processing
  • Using NoSQL and different applied sciences to satisfy massive Data’s special information processing requirements
  • Leveraging statistical methods of quantitative and qualitative analysis
  • Applying computational research tools, together with computing device learning

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Additional resources for Big Data Fundamentals: Concepts, Drivers & Techniques

Sample text

The use of such external data most often results in “Big Data” datasets. Each of these topics will be explored in turn. In this environment, companies conduct transformation projects to improve their corporate processes to achieve savings. Davenport and Prusak have provided generally-accepted working definitions of data, information and knowledge in their book Working Knowledge. Innovation brings hope to a company that it will find new ways to achieve a competitive advantage in the marketplace and a consequent increase in top line revenue.

The IT team attributes this to the data validation performed at multiple stages including validation at the time of data entry, validation at various points when an application is processing data, such as function-level input validation, and validation performed by the database when data is persisted. Looking outside ETI’s boundary, a study of a few samples taken from the social media data and weather data demonstrates further decline in veracity indicating that such data will require an increased level of data validation and cleansing to make it high veracity data.

Value As far as the value characteristic is concerned, all IT team members concur that they need to draw maximum value out of the available datasets by ensuring the datasets are stored in their original form and that they are subjected to the right type of analytics. Identifying Types of Data The IT team members go through a categorization exercise of the various datasets that have been identified up until now and come up with the following list: • Structured data: policy data, claim data, customer profile data and quote data.

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

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