  We discuss the technical challenges we faced and the techniques we used to
overcome them when reducing the PHAT photometric data set on the Amazon Elastic
Compute Cloud (EC2). We first describe the architecture of our photometry
pipeline, which we found particularly efficient for reducing the data in
multiple ways for different purposes. We then describe the features of EC2 that
make this architecture both efficient to use and challenging to implement. We
describe the techniques we adopted to process our data, and suggest ways these
techniques may be improved for those interested in trying such reductions in
the future. Finally, we summarize the output photometry data products, which
are now hosted publicly in two places in two formats. They are in simple fits
tables in the high-level science products on MAST, and on a queryable database
available through the NOAO Data Lab.
