Principles of Big Data

Preparing, Sharing, and Analyzing Complex Information

Nonfiction, Computers, Database Management, Data Processing, General Computing
Cover of the book Principles of Big Data by Jules J. Berman, Elsevier Science
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Author: Jules J. Berman ISBN: 9780124047242
Publisher: Elsevier Science Publication: May 20, 2013
Imprint: Morgan Kaufmann Language: English
Author: Jules J. Berman
ISBN: 9780124047242
Publisher: Elsevier Science
Publication: May 20, 2013
Imprint: Morgan Kaufmann
Language: English

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators.

  • Learn general methods for specifying Big Data in a way that is understandable to humans and to computers
  • Avoid the pitfalls in Big Data design and analysis
  • Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators.

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