 Название: Real-Time & Stream Data Management: Push-Based Data in Research & Practice
Автор: Wolfram Wingerath, Norbert Ritter
Издательство: Springer
ISBN: 3030105547
Год: 2019
Страниц: 84
Язык: английский
Формат: pdf (true), azw3, epub
Размер: 10.17 MB
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 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.
Contents:
1 An Introduction to Real-Time Data Management 1
1. 1 A Brief History of Data Management 2
1. 2 Data Access: Pull vs. Push 4
1. 3 Query Semantics: Collections vs. Streams 5
1. 4 Chapter Outline 6
References 6
2 Database Management 9
2. 1 Triggers and Active Databases 9
2. 2 Change Data Capture, Cache Coherence, and Time-Series Data 10
2. 3 Materialized Views 12
2. 4 Change Notifications 13
2. 5 Summary and Discussion 14
References 14
3 Real-Time Databases 21
3. 1 What Is a Real-Time Database? 21
3. 2 What Is a Real-Time Query? 22
3. 3 System Landscape 24
3. 3. 1 Meteor 24
3. 3. 2 RethinkDB 28
3. 3. 3 Parse 29
3. 3. 4 Firebase 29
3. 3. 5 Baqend 31
3. 3. 6 Further Systems 35
3. 4 Summary and Discussion 36
References 38
4 Data Stream Management 43
4. 1 Queries Over Streams 43
4. 2 Notions of Time 46
4. 3 Windowing and Approximation 47
4. 4 Complex Event Processing 48
4. 5 Messaging Middleware 49
4. 6 Summary and Discussion 49
References 50
5 General-Purpose Stream Processing 57
5. 1 Architectural Patterns 57
5. 2 Batch vs. Stream Processing 60
5. 3 State-of-the-Art Stream Processing Frameworks 61
5. 3. 1 Storm 61
5. 3. 2 Samza 63
5. 3. 3 Spark Streaming 65
5. 3. 4 Flink 66
5. 3. 5 Further Systems 68
5. 4 Design Decisions and Trade-Offs 69
5. 5 Summary and Discussion 69
References 71
6 State of the Art and Future Directions 75
6. 1 The Big Picture 75
6. 2 Real-Time Databases: A New Paradigm for Data Management 76
6. 3 Closing Thoughts 77
Reference 77
Скачать Real-Time & Stream Data Management: Push-Based Data in Research & Practice
|
Навигация
Вход на сайт
Реклама
Real-Time & Stream Data Management: Push-Based Data in Research & Practice 
