Big Stream Processing Systems: An Experimental Evaluation
As the world gets more instrumented and connected, we are witnessing a flood of digital data generated from various hardware (e.g., sensors) or software in the format of flowing streams of data. Real-time processing for such massive amounts of streaming data is a crucial requirement in several appli...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| منشور في: |
2019
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| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/2922 https://ieeexplore.ieee.org/document/8750955 https://doi.org/10.1109/ICDEW.2019.00-35 |
| الوسوم: |
إضافة وسم
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| _version_ | 1862980616938586112 |
|---|---|
| author | Shahverdi, Elkhan |
| author2 | Awad, Ahmed Sakr, Sherif |
| author2_role | author author |
| author_facet | Shahverdi, Elkhan Awad, Ahmed Sakr, Sherif |
| author_role | author |
| dc.creator.none.fl_str_mv | Shahverdi, Elkhan Awad, Ahmed Sakr, Sherif |
| dc.date.none.fl_str_mv | 2019 2025-05-06T08:09:26Z 2025-05-06T08:09:26Z |
| dc.identifier.none.fl_str_mv | Sakr, S. et al. (2019) “Big Stream Processing Systems: An Experimental Evaluation,” in 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), pp. 53–60. . Electronic ISBN:978-1-7281-0890-2 Print on Demand(PoD) ISBN:978-1-7281-0891-9 https://bspace.buid.ac.ae/handle/1234/2922 https://ieeexplore.ieee.org/document/8750955 https://doi.org/10.1109/ICDEW.2019.00-35 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE computer society |
| dc.relation.none.fl_str_mv | IEEE International Conference on Data Engineering |
| dc.title.none.fl_str_mv | Big Stream Processing Systems: An Experimental Evaluation |
| dc.type.none.fl_str_mv | Conference Paper |
| description | As the world gets more instrumented and connected, we are witnessing a flood of digital data generated from various hardware (e.g., sensors) or software in the format of flowing streams of data. Real-time processing for such massive amounts of streaming data is a crucial requirement in several application domains including financial markets, surveillance systems, man ufacturing, smart cities, and scalable monitoring infrastructure. In the last few years, several big stream processing engines have been introduced to tackle this challenge. In this article, we present an extensive experimental study of five popular systems in this domain, namely, Apache Storm, Apache Flink, Apache Spark, Kafka Streams and Hazelcast Jet. We report and analyze the performance characteristics of these systems. In addition, we report a set of insights and important lessons that we have learned from conducting our experiments. |
| id | budr_1ea91f61c34f79710521bc779a9cfe77 |
| identifier_str_mv | Sakr, S. et al. (2019) “Big Stream Processing Systems: An Experimental Evaluation,” in 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), pp. 53–60. . Electronic ISBN:978-1-7281-0890-2 Print on Demand(PoD) ISBN:978-1-7281-0891-9 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2922 |
| publishDate | 2019 |
| publisher.none.fl_str_mv | IEEE computer society |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Big Stream Processing Systems: An Experimental EvaluationShahverdi, ElkhanAwad, AhmedSakr, SherifAs the world gets more instrumented and connected, we are witnessing a flood of digital data generated from various hardware (e.g., sensors) or software in the format of flowing streams of data. Real-time processing for such massive amounts of streaming data is a crucial requirement in several application domains including financial markets, surveillance systems, man ufacturing, smart cities, and scalable monitoring infrastructure. In the last few years, several big stream processing engines have been introduced to tackle this challenge. In this article, we present an extensive experimental study of five popular systems in this domain, namely, Apache Storm, Apache Flink, Apache Spark, Kafka Streams and Hazelcast Jet. We report and analyze the performance characteristics of these systems. In addition, we report a set of insights and important lessons that we have learned from conducting our experiments.IEEE computer society2025-05-06T08:09:26Z2025-05-06T08:09:26Z2019Conference PaperSakr, S. et al. (2019) “Big Stream Processing Systems: An Experimental Evaluation,” in 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), pp. 53–60. .Electronic ISBN:978-1-7281-0890-2 Print on Demand(PoD) ISBN:978-1-7281-0891-9https://bspace.buid.ac.ae/handle/1234/2922https://ieeexplore.ieee.org/document/8750955https://doi.org/10.1109/ICDEW.2019.00-35enIEEE International Conference on Data Engineering oai:bspace.buid.ac.ae:1234/29222025-06-13T10:59:19Z |
| spellingShingle | Big Stream Processing Systems: An Experimental Evaluation Shahverdi, Elkhan |
| title | Big Stream Processing Systems: An Experimental Evaluation |
| title_full | Big Stream Processing Systems: An Experimental Evaluation |
| title_fullStr | Big Stream Processing Systems: An Experimental Evaluation |
| title_full_unstemmed | Big Stream Processing Systems: An Experimental Evaluation |
| title_short | Big Stream Processing Systems: An Experimental Evaluation |
| title_sort | Big Stream Processing Systems: An Experimental Evaluation |
| url | https://bspace.buid.ac.ae/handle/1234/2922 https://ieeexplore.ieee.org/document/8750955 https://doi.org/10.1109/ICDEW.2019.00-35 |