Table of contents
About this book
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 pushoriented 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.
Real-time databases database management data stream management stream processing big data management reactive applications real-time queries push-based data retrieval real-time database survey data management system classification
- Book Title Real-Time & Stream Data Management
- Book Subtitle Push-Based Data in Research & Practice
- Series Title SpringerBriefs in Computer Science
- Series Abbreviated Title SpringerBriefs Computer Sci.
- DOI https://doi.org/10.1007/978-3-030-10555-6
- Copyright Information The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
- Publisher Name Springer, Cham
- eBook Packages Computer Science Computer Science (R0)
- Softcover ISBN 978-3-030-10554-9
- eBook ISBN 978-3-030-10555-6
- Series ISSN 2191-5768
- Series E-ISSN 2191-5776
- Edition Number 1
- Number of Pages IX, 77
- Number of Illustrations 9 b/w illustrations, 6 illustrations in colour
Information Storage and Retrieval
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