  Leveraging their eBird crowdsourcing project, the Cornell Lab of Ornithology
generates sophisticated Spatio-Temporal Exploratory Model (STEM) maps of bird
migrations. Such maps are highly relevant for both scientific and educational
purposes, but creating them requires advanced modeling techniques that rely on
long and potentially expensive computations.
  In this paper, we share our experience porting the eBird STEM data pipeline
from a physical cluster to the cloud, providing a seamless deployment at a
lower cost. Using open source tools and cloud "marketplaces", we managed to
divide the operating costs by a factor of 6, making it possible to scale our
pipeline on a research budget.
