We have architected and evaluated a new kind of data resource, one that is composed of a logical collection of ephemeral data streams that could be viewed as a collection of publish-subscribe "channels" over which rich dataaccess and semantic operations can be performed. This paper contributes new insight to stream processing under the highly asynchronous stream workloads often found in datadriven scientific applications, and presents insights gained through porting a distributed stream processing system to a Grid services framework. Experimental results reveal limits on stream processing rates that are directly tied to differences in stream rates.