Dataflow is a technique for parallel computing that emerged from research in the 1970s. It's based on graph-based execution models where data flows along the arcs on a graph and is processed at the nodes. It was decades ahead of its time in an era when hardware was expensive and real-world requirements for massively parallel, low latency computing architectures were not required in the mainstream. However, dataflow as an architecture has found its place and time, with the emergence of Big Data volume, real-time low latency requirements, commodity hardware and low cost storage. Dataflow is driving the architectures for today's real-time big data solutions.