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Rapport Année : 2024

Big data stream processing

Big data stream processing

Ovidiu-Cristian Marcu
  • Fonction : Auteur
  • PersonId : 1106444
Pascal Bouvry
  • Fonction : Auteur
  • PersonId : 866914

Résumé

This chapter provides students, industry experts, and researchers a high-level and comprehensive overview of the end-to-end architectures of big data stream processing pipelines, designed to keep them informed of the latest advancements and best practices in the field. It explores crucial components such as source ingestion, state and storage mechanisms, stream query processing, and distributed streaming. Streaming architectural pipelines manage continuous data flows from various sources, necessitating efficient ingestion, processing, and storage systems. These systems are vital for applications that require real-time data processing, including fraud detection, IoT monitoring, patient health tracking, and e-commerce personalization. By integrating edge, cloud, and high-performance computing environments, big data stream processing ensures low latency and high throughput. Each section of this chapter delves into the most critical topics in stream processing, referencing recent literature for further exploration and offering insights into ongoing technological developments.
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Dates et versions

hal-04687320 , version 1 (04-09-2024)

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  • HAL Id : hal-04687320 , version 1

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Ovidiu-Cristian Marcu, Pascal Bouvry. Big data stream processing. University of Luxembourg. 2024. ⟨hal-04687320⟩

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