  Big Data may not be the solution many are looking for. The latest rise of Big
Data methods and systems is partly due to the new abilities these techniques
provide, partly to the simplicity of the software design and partly because the
buzzword itself has value to investors and clients. That said, popularity is
not a measure for suitability and the Big Data approach might not be the best
solution, or even an applicable one, to many common problems. Namely, time
dependent problems whose solution may be bound or cached in any manner can
benefit greatly from moving to partly stateless, flow oriented functions and
data models. This paper presents such a model to substitute the traditional
map-shuffle-reduce models.
