A substantial part of the " big data " generated today is received in near real time and must be promptly processed. Cloud-based architectures for data stream processing comprise multiple software modules or frameworks for data collection, message queueing, and stream processing itself. This modular approach allows each component to grow independently from one another and accommodate changes, but it may increase the end-to-end latency when data events are processed in the cloud. Recent solutions intend to explore the edges of the Internet (i.e. edge computing) to perform certain data processing tasks and hence better utilise network resources. This work evaluates the impact regarding network bandwidth while employing frameworks that are commonly used to build cloud and edge-based stream processing solutions. Mots-clés : Traitement de flux de données, méta-données, analyse de performance, systèmes distribués, informatique en nuages