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 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 M500c greater than or similar to 10(14) h(70)(-1)M(circle dot)) and 0.2 <z <0.9. The DR8 catalog consists of 26,311 clusters with 0.08 <z <0.6, with a sharply increasing richness threshold as a function of redshift for z greater than or similar to 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) similar to 0.01 level for z greater than or similar to 0.7, rising to similar to 0.02 at z similar to 0.9 in DES SV. We make use of Chandra and 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 photo-z and richness estimates are relatively insensitive to imperfect star/galaxy separation and small-scale star masks.