Standard

Large-scale data stream processing systems. / Carbone, Paris; Gévay, Gábor E.; Hermann, Gábor; Katsifodimos, Asterios; Soto, Juan; Markl, Volker; Haridi, Seif.

Handbook of Big Data Technologies. ed. / A.Y. Zomaya; S. Sherif. 1. ed. Cham : Springer International Publishing, 2017. p. 219-260.

Research output: ScientificChapter

Harvard

Carbone, P, Gévay, GE, Hermann, G, Katsifodimos, A, Soto, J, Markl, V & Haridi, S 2017, Large-scale data stream processing systems. in AY Zomaya & S Sherif (eds), Handbook of Big Data Technologies. 1 edn, Springer International Publishing, Cham, pp. 219-260. DOI: 10.1007/978-3-319-49340-4_7

APA

Carbone, P., Gévay, G. E., Hermann, G., Katsifodimos, A., Soto, J., Markl, V., & Haridi, S. (2017). Large-scale data stream processing systems. In A. Y. Zomaya, & S. Sherif (Eds.), Handbook of Big Data Technologies (1 ed., pp. 219-260). Cham: Springer International Publishing. DOI: 10.1007/978-3-319-49340-4_7

Vancouver

Carbone P, Gévay GE, Hermann G, Katsifodimos A, Soto J, Markl V et al. Large-scale data stream processing systems. In Zomaya AY, Sherif S, editors, Handbook of Big Data Technologies. 1 ed. Cham: Springer International Publishing. 2017. p. 219-260. Available from, DOI: 10.1007/978-3-319-49340-4_7

Author

Carbone, Paris ; Gévay, Gábor E. ; Hermann, Gábor ; Katsifodimos, Asterios ; Soto, Juan ; Markl, Volker ; Haridi, Seif. / Large-scale data stream processing systems. Handbook of Big Data Technologies. editor / A.Y. Zomaya ; S. Sherif. 1. ed. Cham : Springer International Publishing, 2017. pp. 219-260

BibTeX

@inbook{d1474663f41c43699fa417da3a51f210,
title = "Large-scale data stream processing systems",
abstract = "In our data-centric society, online services, decision making, and other aspects are increasingly becoming heavily dependent on trends and patterns extracted from data. A broad class of societal-scale data management problems requires system support for processing unbounded data with low latency and high throughput. Large-scale data stream processing systems perceive data as infinite streams and are designed to satisfy such requirements. They have further evolved substantially both in terms of expressive programming model support and also efficient and durable runtime execution on commodity clusters. Expressive programming models offer convenient ways to declare continuous data properties and applied computations, while hiding details on how these data streams are physically processed and orchestrated in a distributed environment. Execution engines provide a runtime for such models further allowing for scalable yet durable execution of any declared computation. In this chapter we introduce the major design aspects of large scale data stream processing systems, covering programming model abstraction levels and runtime concerns. We then present a detailed case study on stateful stream processing with Apache Flink, an open-source stream processor that is used for a wide variety of processing tasks. Finally, we address the main challenges of disruptive applications that large-scale data streaming enables from a systemic point of view.",
keywords = "Harness",
author = "Paris Carbone and Gévay, {Gábor E.} and Gábor Hermann and Asterios Katsifodimos and Juan Soto and Volker Markl and Seif Haridi",
year = "2017",
month = "2",
doi = "10.1007/978-3-319-49340-4_7",
isbn = "978-3-319-49339-8",
pages = "219--260",
editor = "A.Y. Zomaya and S. Sherif",
booktitle = "Handbook of Big Data Technologies",
publisher = "Springer International Publishing",
edition = "1",

}

RIS

TY - CHAP

T1 - Large-scale data stream processing systems

AU - Carbone,Paris

AU - Gévay,Gábor E.

AU - Hermann,Gábor

AU - Katsifodimos,Asterios

AU - Soto,Juan

AU - Markl,Volker

AU - Haridi,Seif

PY - 2017/2/25

Y1 - 2017/2/25

N2 - In our data-centric society, online services, decision making, and other aspects are increasingly becoming heavily dependent on trends and patterns extracted from data. A broad class of societal-scale data management problems requires system support for processing unbounded data with low latency and high throughput. Large-scale data stream processing systems perceive data as infinite streams and are designed to satisfy such requirements. They have further evolved substantially both in terms of expressive programming model support and also efficient and durable runtime execution on commodity clusters. Expressive programming models offer convenient ways to declare continuous data properties and applied computations, while hiding details on how these data streams are physically processed and orchestrated in a distributed environment. Execution engines provide a runtime for such models further allowing for scalable yet durable execution of any declared computation. In this chapter we introduce the major design aspects of large scale data stream processing systems, covering programming model abstraction levels and runtime concerns. We then present a detailed case study on stateful stream processing with Apache Flink, an open-source stream processor that is used for a wide variety of processing tasks. Finally, we address the main challenges of disruptive applications that large-scale data streaming enables from a systemic point of view.

AB - In our data-centric society, online services, decision making, and other aspects are increasingly becoming heavily dependent on trends and patterns extracted from data. A broad class of societal-scale data management problems requires system support for processing unbounded data with low latency and high throughput. Large-scale data stream processing systems perceive data as infinite streams and are designed to satisfy such requirements. They have further evolved substantially both in terms of expressive programming model support and also efficient and durable runtime execution on commodity clusters. Expressive programming models offer convenient ways to declare continuous data properties and applied computations, while hiding details on how these data streams are physically processed and orchestrated in a distributed environment. Execution engines provide a runtime for such models further allowing for scalable yet durable execution of any declared computation. In this chapter we introduce the major design aspects of large scale data stream processing systems, covering programming model abstraction levels and runtime concerns. We then present a detailed case study on stateful stream processing with Apache Flink, an open-source stream processor that is used for a wide variety of processing tasks. Finally, we address the main challenges of disruptive applications that large-scale data streaming enables from a systemic point of view.

KW - Harness

UR - http://www.scopus.com/inward/record.url?scp=85019960984&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-49340-4_7

DO - 10.1007/978-3-319-49340-4_7

M3 - Chapter

SN - 978-3-319-49339-8

SP - 219

EP - 260

BT - Handbook of Big Data Technologies

PB - Springer International Publishing

ER -

ID: 36128947