Institution(s): 1. University of Hawaii
For even small telescope projects, the petabyte scale is now upon us. The Asteroid Terrestrial-impact Last Alert System (ATLAS; Tonry 2011) will robotically survey the entire visible sky from Hawaii multiple times per night to search for near-Earth asteroids (NEAs) on impact trajectories. While the ATLAS optical system is modest by modern astronomical standards -- two 0.5 m F/2.0 telescopes -- each year the ATLAS system will obtain ~103 measurements of 109 astronomical sources to a photometric accuracy of <5%. This ever-growing dataset must be searched in real-time for moving objects then archived for further analysis, and alerts for newly discovered near-Earth NEAs disseminated within tens of minutes from detection. ATLAS's all-sky coverage ensures it will discover many ``rifle shot'' near-misses moving rapidly on the sky as they shoot past the Earth, so the system will need software to automatically detect highly-trailed sources and discriminate them from the thousands of satellites and pieces of space junk that ATLAS will see each night. Additional interrogation will identify interesting phenomena from beyond the solar system occurring over millions of transient sources per night. The data processing and storage requirements for ATLAS demand a ``big data'' approach typical of commercial Internet enterprises. We describe our approach to deploying a nimble, scalable and reliable data processing infrastructure, and promote ATLAS as steppingstone to eventual processing scales in the era of LSST.