  Emerging data-driven scientific workflows are seeking to leverage distributed
data sources to understand end-to-end phenomena, drive experimentation, and
facilitate important decision-making. Despite the exponential growth of
available digital data sources at the edge, and the ubiquity of non trivial
computational power for processing this data, realizing such science workflows
remains challenging. This paper explores a computing continuum that is
everywhere and nowhere -- one spanning resources at the edges, in the core and
in between, and providing abstractions that can be harnessed to support
science. It also introduces recent research in programming abstractions that
can express what data should be processed and when and where it should be
processed, and autonomic middleware services that automate the discovery of
resources and the orchestration of computations across these resources.
