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

By Thomas Erl, Wajid Khattak, Paul Buhler

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

“The one ebook that in actual fact describes and hyperlinks sizeable info options to enterprise utility.”
--Dr. Christopher Starr, PhD

“Simply, this is often the easiest sizeable 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 advisor to important info for enterprise and know-how execs

Big information basics provides a realistic, no-nonsense advent to special info. Best-selling IT writer Thomas Erl and his workforce in actual fact clarify key enormous facts suggestions, conception and terminology, in addition to primary applied sciences and methods. All insurance is supported with case learn examples and various uncomplicated diagrams.

The authors commence through explaining how giant facts can propel a company ahead by means of fixing a spectrum of formerly intractable company difficulties. subsequent, they demystify key research ideas and applied sciences and express how an important information answer surroundings could be outfitted and built-in to provide aggressive advantages.

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

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

Example text

Are the results of the analysis being accurately communicated to the appropriate decision-makers? Different Types of Data The data processed by Big Data solutions can be human-generated or machine-generated, although it is ultimately the responsibility of machines to generate the analytic results. 16 shows examples of human-generated data. 16 Examples of human-generated data include social media, blog posts, emails, photo sharing and messaging. 17 provides a visual representation of different types of machinegenerated data.

This includes performing drill-down operations to breakdown sales by type and location so that it can be determined which locations underperformed for specific types of policies. 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.

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. • Unstructured data: social media data, insurance application documents, call center agent notes, claim adjuster notes and incident photographs. • Semi-structured data: health records, customer profile data, weather reports, census data, webserver logs and emails.

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