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Real-time & stream data management : push-based data in research & practice

Author: Wolfram Wingerath; Norbert Ritter; Felix Gessert
Publisher: Cham, Switzerland : Springer, 2019.
Series: SpringerBriefs in computer science
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Wingerath, Wolfram.
Real-time & stream data management.
Cham, Switzerland : Springer, 2019
(OCoLC)1076229340
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Wolfram Wingerath; Norbert Ritter; Felix Gessert
ISBN: 9783030105556 3030105555
OCLC Number: 1082356647
Description: 1 online resource (ix, 77 pages) : illustrations (some color)
Contents: Intro; Preface; Contents; About the Authors; 1 An Introduction to Real-Time Data Management; 1.1 A Brief History of Data Management; 1.2 Data Access: Pull vs. Push; 1.3 Query Semantics: Collections vs. Streams; 1.4 Chapter Outline; References; 2 Database Management; 2.1 Triggers and Active Databases; 2.2 Change Data Capture, Cache Coherence, and Time-Series Data; 2.3 Materialized Views; 2.4 Change Notifications; 2.5 Summary and Discussion; References; 3 Real-Time Databases; 3.1 What Is a Real-Time Database?; 3.2 What Is a Real-Time Query?; 3.3 System Landscape; 3.3.1 Meteor Change Monitoring + Poll-and-DiffOplog Tailing; 3.3.2 RethinkDB; Changefeeds; 3.3.3 Parse; Live Queries; 3.3.4 Firebase; Data Modeling and Querying; Advanced Queries with Firestore; 3.3.5 Baqend; Two-Dimensional Workload Partitioning for Linear Scalability; 3.3.6 Further Systems; 3.4 Summary and Discussion; References; 4 Data Stream Management; 4.1 Queries Over Streams; 4.2 Notions of Time; 4.3 Windowing and Approximation; 4.4 Complex Event Processing; 4.5 Messaging Middleware; 4.6 Summary and Discussion; References; 5 General-Purpose Stream Processing; 5.1 Architectural Patterns 5.2 Batch vs. Stream Processing5.3 State-of-the-Art Stream Processing Frameworks; 5.3.1 Storm; Storm Trident; 5.3.2 Samza; 5.3.3 Spark Streaming; 5.3.4 Flink; 5.3.5 Further Systems; 5.4 Design Decisions and Trade-Offs; 5.5 Summary and Discussion; References; 6 State of the Art and Future Directions; 6.1 The Big Picture; 6.2 Real-Time Databases: A New Paradigm for Data Management; 6.3 Closing Thoughts; Reference
Series Title: SpringerBriefs in computer science
Responsibility: Wolfram Wingerath, Norbert Ritter, Felix Gessert.

Abstract:

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  Read more...

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