  We describe updates to the \redmapper{} algorithm, a photometric red-sequence
cluster finder specifically designed for large photometric surveys. The updated
algorithm is applied to $150\,\mathrm{deg}^2$ of Science Verification (SV) data
from the Dark Energy Survey (DES), and to the Sloan Digital Sky Survey (SDSS)
DR8 photometric data set. The DES SV catalog is locally volume limited, and
contains 786 clusters with richness $\lambda>20$ (roughly equivalent to
$M_{\rm{500c}}\gtrsim10^{14}\,h_{70}^{-1}\,M_{\odot}$) and $0.2<z<0.9$. The DR8
catalog consists of 26311 clusters with $0.08<z<0.6$, with a sharply increasing
richness threshold as a function of redshift for $z\gtrsim 0.35$. The
photometric redshift performance of both catalogs is shown to be excellent,
with photometric redshift uncertainties controlled at the $\sigma_z/(1+z)\sim
0.01$ level for $z\lesssim0.7$, rising to $\sim0.02$ at $z\sim0.9$ in DES SV.
We make use of \emph{Chandra} and \emph{XMM} X-ray and South Pole Telescope
Sunyaev-Zeldovich data to show that the centering performance and
mass--richness scatter are consistent with expectations based on prior runs of
\redmapper{} on SDSS data. We also show how the \redmapper{} \photoz{} and
richness estimates are relatively insensitive to imperfect star/galaxy
separation and small-scale star masks.
