The increasing deployment of sensor network infrastructures (in a variety of applications, ranging from environmental monitoring, "Smart Cities", energy demand forecasting, social media analysis to emergency response) has led to large volumes of data becoming available, leading to new challenges in storing, processing, analysing and transmitting such data. This is especially true when data from multiple sensors is pre-processed prior to delivery to users. Where such data is processed in-transit (i.e. from data capture to delivery to a user) over a shared distributed computing infrastructure, it is necessary to provide some Quality of Service (QoS) guarantees to each user. This talk will explore the emerging interest in real time, streaming data that needs to be processed within particular time bounds. Whereas significant investments have taken place in managing file-based data, coordinating the analysis and management of data streams remains a challenge. The first part of this talk is a tutorial-style introduction to stream processing and key research challenges in this area. The second part investigates how adaptive computing environments (such as Cloud computing) can be used to processing stream-based data.