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Streams and Tables: Two Sides of the Same Coin
Open accessAbstract
Stream processing has emerged as a paradigm for applications that require low-latency evaluation of operators over unbounded sequences of data. Defining the semantics of stream processing is challenging in the presence of distributed data sources. The physical and logical order of data in a stream may become inconsistent in such a setting. Existing models either neglect these inconsistencies or handle them by means of data buffering and reordering techniques, thereby compromising processing latency.
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Apache Kafka Streams has a restricted set of features compared to most other stream processing frameworks, particularly concerning integration with external systems.
“Compared to most other stream processing frameworks, it has a restricted set of features, in particular, concerning the integration with external systems.”
From Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud (2024) · cites this paper as Sax et al., 2018
Section 5 describes Kafka Streams’ processing features and its use of Kafka topics, but does not compare its feature set with other frameworks. A search for external-system integration and comparative limitations found no passage addressing the claim. (LLM, conf=0.73)
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Research works printed but not linked: 26
- 10.1007/s00778-003-0095-z · DOI
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Cited by
Sorted by citations
Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
Henning, S., & Hasselbring, W. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111879 · Cited by 85
Metadata
- Authors
- Sax, M. J., Wang, G., Weidlich, M., & Freytag, J.-C.
- Affiliations
- Confluent Inc., Palo Alto, USA
- Humboldt-Universität zu Berlin (DE)
- Venue
- Proceedings of the International Workshop on Real-Time Business Intelligence and Analytics
- Publisher
- ACM
- Published
- 2018-08-27
- Pages
- 1-10
- OpenAlex
- W2892199102
- Cited by
- 51
- References
- 0
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 6 · 2023: 6 · 2022: 4 · 2021: 11 · 2020: 4 · 2019: 6
- FWCI
- 3.51
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Topics
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Keywords
- stream processing, distributed data sources, data inconsistency, data buffering, reordering
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