Making sense of stream processing: the philosophy behind Apache Kafka and scalable stream data platforms
How can event streams help make your application more scalable, reliable, and maintainable? In this report, O'Reilly author Martin Kleppmann shows you how stream processing can make your data storage and processing systems more flexible and less complex. Structuring data as a stream of events i...
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| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Sebastopol, CA
O'Reilly Media
2016
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| Edition: | First edition. |
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781492042563/?ar |
| Summary: | How can event streams help make your application more scalable, reliable, and maintainable? In this report, O'Reilly author Martin Kleppmann shows you how stream processing can make your data storage and processing systems more flexible and less complex. Structuring data as a stream of events isn't new, but with the advent of open source projects such as Apache Kafka and Apache Samza, stream processing is finally coming of age. Using several case studies, Kleppmann explains how these projects can help you reorient your database architecture around streams and materialized views. The benefits of this approach include better data quality, faster queries through precomputed caches, and real-time user interfaces. Learn how to open up your data for richer analysis and make your applications more scalable and robust in the face of failures. Understand stream processing fundamentals and their similarities to event sourcing, CQRS, and complex event processing Learn how logs can make search indexes and caches easier to maintain Explore the integration of databases with event streams, using the new Bottled Water open source tool Turn your database architecture inside out by orienting it around streams and materialized views. |
| Item Description: | Online resource; title from title page (viewed January 10, 2019) |
| Physical Description: | 1 Online-Ressource (1 volume) Illustrationen |
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| spelling | Kleppmann, Martin VerfasserIn aut Making sense of stream processing the philosophy behind Apache Kafka and scalable stream data platforms Martin Kleppmann First edition. Sebastopol, CA O'Reilly Media 2016 1 Online-Ressource (1 volume) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online resource; title from title page (viewed January 10, 2019) How can event streams help make your application more scalable, reliable, and maintainable? In this report, O'Reilly author Martin Kleppmann shows you how stream processing can make your data storage and processing systems more flexible and less complex. Structuring data as a stream of events isn't new, but with the advent of open source projects such as Apache Kafka and Apache Samza, stream processing is finally coming of age. Using several case studies, Kleppmann explains how these projects can help you reorient your database architecture around streams and materialized views. The benefits of this approach include better data quality, faster queries through precomputed caches, and real-time user interfaces. Learn how to open up your data for richer analysis and make your applications more scalable and robust in the face of failures. Understand stream processing fundamentals and their similarities to event sourcing, CQRS, and complex event processing Learn how logs can make search indexes and caches easier to maintain Explore the integration of databases with event streams, using the new Bottled Water open source tool Turn your database architecture inside out by orienting it around streams and materialized views. Electronic data processing Big data Data processing Data mining SQL (Computer program language) Data Mining Données volumineuses ; Informatique Exploration de données (Informatique) SQL (Langage de programmation) |
| spellingShingle | Kleppmann, Martin Making sense of stream processing the philosophy behind Apache Kafka and scalable stream data platforms Electronic data processing Big data Data processing Data mining SQL (Computer program language) Data Mining Données volumineuses ; Informatique Exploration de données (Informatique) SQL (Langage de programmation) |
| title | Making sense of stream processing the philosophy behind Apache Kafka and scalable stream data platforms |
| title_auth | Making sense of stream processing the philosophy behind Apache Kafka and scalable stream data platforms |
| title_exact_search | Making sense of stream processing the philosophy behind Apache Kafka and scalable stream data platforms |
| title_full | Making sense of stream processing the philosophy behind Apache Kafka and scalable stream data platforms Martin Kleppmann |
| title_fullStr | Making sense of stream processing the philosophy behind Apache Kafka and scalable stream data platforms Martin Kleppmann |
| title_full_unstemmed | Making sense of stream processing the philosophy behind Apache Kafka and scalable stream data platforms Martin Kleppmann |
| title_short | Making sense of stream processing |
| title_sort | making sense of stream processing the philosophy behind apache kafka and scalable stream data platforms |
| title_sub | the philosophy behind Apache Kafka and scalable stream data platforms |
| topic | Electronic data processing Big data Data processing Data mining SQL (Computer program language) Data Mining Données volumineuses ; Informatique Exploration de données (Informatique) SQL (Langage de programmation) |
| topic_facet | Electronic data processing Big data Data processing Data mining SQL (Computer program language) Data Mining Données volumineuses ; Informatique Exploration de données (Informatique) SQL (Langage de programmation) |
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