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

By Thomas Erl, Wajid Khattak, Paul Buhler

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

“The one e-book that sincerely describes and hyperlinks giant information recommendations to company utility.”
--Dr. Christopher Starr, PhD

“Simply, this is often the simplest large info ebook at the market!”
--Sam Rostam, Cascadian IT Group

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

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

Big information basics provides a realistic, no-nonsense advent to important information. Best-selling IT writer Thomas Erl and his staff basically clarify key giant facts techniques, concept and terminology, in addition to basic applied sciences and methods. All assurance is supported with case learn examples and various easy diagrams.

The authors start via explaining how massive information can propel a company ahead through fixing a spectrum of formerly intractable company difficulties. subsequent, they demystify key research innovations and applied sciences and express how an important info answer setting may be equipped and built-in to provide aggressive advantages.

  • Discovering vast Data’s primary thoughts and what makes it various from past sorts of info research and knowledge science
  • Understanding the company motivations and drivers in the back of large info adoption, from operational advancements via innovation
  • Planning strategic, business-driven monstrous info initiatives
  • Addressing issues similar to info administration, governance, and security
  • Recognizing the five “V” features of datasets in vast info environments: quantity, speed, kind, veracity, and value
  • Clarifying great Data’s relationships with OLTP, OLAP, ETL, info warehouses, and information marts
  • Working with large facts in dependent, unstructured, semi-structured, and metadata formats
  • Increasing price by way of integrating enormous information assets with company functionality monitoring
  • Understanding how tremendous info leverages allotted and parallel processing
  • Using NoSQL and different applied sciences to satisfy huge Data’s particular info processing requirements
  • Leveraging statistical methods of quantitative and qualitative analysis
  • Applying computational research tools, together with computer learning

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Extra info for Big Data Fundamentals: Concepts, Drivers & Techniques (The Prentice Hall Service Technology Series from Thomas Erl)

Sample text

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. This data includes health records, documents submitted by the customers at the time of submitting an insurance application, property schedules, fleet data, social media data and weather data.

In either case, the ability of a cloud to dynamically scale based upon load allows for the creation of resilient analytic environments that maximize efficient utilization of ICT resources. The fact that off-premise cloudbased IT resources can be leased dramatically reduces the required up-front investment of Big Data projects. 7 The cloud can be used to complete on-demand data analysis at the end of each month or enable the scaling out of systems with an increase in load. It makes sense for enterprises already using cloud computing to reuse the cloud for their Big Data initiatives because: • personnel already possesses the required cloud computing skills • the input data already exists in the cloud Migrating to the cloud is logical for enterprises planning to run analytics on datasets that are available via data markets, as many data markets make their datasets available in a cloud environment, such as Amazon S3.

5 provides an example of the price decline associated with data storage prices over the past 20 years. 10 per GB over the decades. For this reason, businesses are increasingly interested in incorporating publicly available datasets from social media and other external data sources. This requires detailed analysis of sensor readings emitted by the equipment for the early detection of issues that can be resolved via the proactive scheduling of maintenance activities. 6 Hyper-connected communities and devices include television, mobile computing, RFIDs, refrigerators, GPS devices, mobile devices and smart meters.

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