NebulaStream - Data Stream Processing for the Edge-Cloud-Continuum: Course 1

Modern data-driven applications arising in such domains as smart manufacturing, healthcare, and the Internet of Things, pose new challenges to data processing systems. Traditional stream processing systems, such as Flink, Spark, and Kafka Streams are ill-suited to cope with the massive scale of distribution, the heterogeneous computing landscape, and requirements, such as timely processing and actuation. Classical approaches like managed runtimes, interpretation-based query processing, and the optimization of single queries that neglect interactions, greatly limit throughput, latency, energy-efficiency, and the general usability of these systems for emerging applications involving distributed data processing at scale in a sensor-edge-cloud-environment.

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Published
2025-06-30
DOI
https://doi.org/10.1145/3769394.3769415
Primary Topic
Scientific Computing and Data Management
Type
article
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article

NebulaStream - Data Stream Processing for the Edge-Cloud-Continuum: Course 1

Volker Markl
Scientific Computing and Data Management
article

NebulaStream - Data Stream Processing for the Edge-Cloud-Continuum: Course 1

Volker Markl
article en

Abstract

Modern data-driven applications arising in such domains as smart manufacturing, healthcare, and the Internet of Things, pose new challenges to data processing systems. Traditional stream processing systems, such as Flink, Spark, and Kafka Streams are ill-suited to cope with the massive scale of distribution, the heterogeneous computing landscape, and requirements, such as timely processing and actuation. Classical approaches like managed runtimes, interpretation-based query processing, and the optimization of single queries that neglect interactions, greatly limit throughput, latency, energy-efficiency, and the general usability of these systems for emerging applications involving distributed data processing at scale in a sensor-edge-cloud-environment.

Technische Universität Berlin (DE)
Openalex Percentile: Top 73%
Scientific Computing and Data Management
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