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

February 2, 2018 | Data Mining | By admin | 0 Comments

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 booklet that sincerely describes and hyperlinks mammoth info options to company utility.”
--Dr. Christopher Starr, PhD

“Simply, this can be the simplest substantial information booklet at the market!”
--Sam Rostam, Cascadian IT Group

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

The Definitive Plain-English consultant to important info for enterprise and expertise execs

Big info basics provides a practical, no-nonsense creation to important information. Best-selling IT writer Thomas Erl and his workforce in actual fact clarify key gigantic info options, thought and terminology, in addition to basic applied sciences and methods. All assurance is supported with case learn examples and diverse basic diagrams.

The authors start through explaining how gigantic facts can propel a company ahead by way of fixing a spectrum of formerly intractable enterprise difficulties. subsequent, they demystify key research suggestions and applied sciences and express how an immense info answer atmosphere may be outfitted and built-in to supply aggressive advantages.

  • Discovering mammoth Data’s basic thoughts and what makes it diversified from earlier different types of info research and knowledge science
  • Understanding the enterprise motivations and drivers in the back of great info adoption, from operational advancements via innovation
  • Planning strategic, business-driven massive information initiatives
  • Addressing issues corresponding to facts administration, governance, and security
  • Recognizing the five “V” features of datasets in great facts environments: quantity, pace, style, veracity, and value
  • Clarifying substantial Data’s relationships with OLTP, OLAP, ETL, information warehouses, and information marts
  • Working with gigantic information in based, unstructured, semi-structured, and metadata formats
  • Increasing worth through integrating massive information assets with company functionality monitoring
  • Understanding how colossal information leverages disbursed and parallel processing
  • Using NoSQL and different applied sciences to satisfy titanic Data’s certain info processing requirements
  • Leveraging statistical ways of quantitative and qualitative analysis
  • Applying computational research equipment, together with desktop learning

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

Example text

Value is also dependent on how long data processing takes because analytics results have a shelf-life; for example, a 20 minute delayed stock quote has little to no value for making a trade compared to a quote that is 20 milliseconds old. 15 provides two illustrations of how value is impacted by the veracity of data and the timeliness of generated analytic results. 15 Data that has high veracity and can be analyzed quickly has more value to a business. Apart from veracity and time, value is also impacted by the following lifecycle-related concerns: • How well has the data been stored?

ETI has decided to implement these two types of analytics in a gradual manner by first implementing predictive analytics and then slowly building up their capabilities to implement prescriptive analytics. For example, prescriptive analytics can prescribe the correct premium amount considering all risk factors or can prescribe the best course of action to take for mitigating claims when faced with catastrophes, such as floods or storms. The team members take each characteristic in turn and discuss how different datasets manifest that characteristic.

Thus the signalto-noise ratio of data is dependent upon the source of the data and its type. Value is also dependent on how long data processing takes because analytics results have a shelf-life; for example, a 20 minute delayed stock quote has little to no value for making a trade compared to a quote that is 20 milliseconds old. 15 provides two illustrations of how value is impacted by the veracity of data and the timeliness of generated analytic results. 15 Data that has high veracity and can be analyzed quickly has more value to a business.

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


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