| Description |
1 online resource (ix, 77 pages) : illustrations (some color) |
| Series |
SpringerBriefs in computer science, 2191-5768 |
|
SpringerBriefs in computer science, 2191-5768
|
| 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 |
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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 |
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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 |
| 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 will first provide an overview over the different (push-based) mechanisms for data retrieval in each system class and the semantic differences between them. It will also provide a comprehensive overview over the current state of the art in real-time databases. It will first include 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 |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed January 16, 2019) |
| Subject |
Database management.
|
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Push technology (Computer networks)
|
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Big data.
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Big data
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Database management
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Push technology (Computer networks)
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| Form |
Electronic book
|
| Author |
Ritter, Norbert, author.
|
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Gessert, Felix, author
|
| ISBN |
9783030105556 |
|
3030105555 |
|
9783030105563 |
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3030105563 |
|
