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Real-Time & Stream Data Management: Push-Based Data in Research & Practice (SpringerBriefs in Computer Science)

Real-Time & Stream Data Management: Push-Based Data in Research & Practice (SpringerBriefs in Computer Science)

Real-Time & Stream Data Management: Push-Based Data in Research &
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Real-Time & Stream Data Management: Push-Based Data in Research & Practice (SpringerBriefs in Computer Science) Paperback - 2019

by Wingerath, Wolfram

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Springer, 2019-01-14. 1st ed. 2019. paperback. New. 6.10x0.20x9.25. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Real-Time & Stream Data Management: Push-Based Data in Research & Practice (SpringerBriefs in Computer Science)
  • Author Wingerath, Wolfram
  • Binding Paperback
  • Edition 1st ed. 2019
  • Condition New
  • Pages 77
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2019-01-14
  • Illustrated Yes
  • Features Illustrated
  • Bookseller's Inventory # DADAX3030105547
  • ISBN 9783030105549 / 3030105547
  • Weight 0.3 lbs (0.14 kg)
  • Dimensions 9.21 x 6.14 x 0.18 in (23.39 x 15.60 x 0.46 cm)
  • Size 6.10x0.20x9.25
  • Category Computers - General Information
  • Dewey Decimal Code 005.73
  • Quantity available 6

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From the publisher

While traditional databases excel at complex queries over historical data, they are inherently pull-based and therefore ill-equipped to push new information to clients. Systems for data stream management and processing, on the other hand, are natively push-oriented and thus facilitate reactive behavior. However, they do not retain data indefinitely and are therefore not able to answer historical queries. The book provides an overview over the different (push-based) mechanisms for data retrieval in each system class and the semantic differences between them. It also provides a comprehensive overview over the current state of the art in real-time databases.
It sfirst includes an in-depth system survey of today's real-time databases: Firebase, Meteor, RethinkDB, Parse, Baqend, and others. Second, the high-level classification scheme illustrated above provides a gentle introduction into the system space of data management: Abstracting from the extreme system diversity in this field, it helps readers build a mental model of the available options.

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