With a new generation of large etendue (the product of field of view and mirror area) survey telescopes there is a growing need for astronomical alert processing systems. Astronomical alerts correspond to detected changes in the sky with an astrophysical origin. Astronomical alert processing systems involve the real–time processing of data for alert generation, real–time annotation and classification of alerts (10 million events per night for LSST) and real–time reaction to interesting alerts using available astronomical resources. We are building a new alert classification and reaction system called ALeRCE: Automatic Learning for the Rapid Classification of Events. ALeRCE is an initiative led by an interdisciplinary and interinstitutional group of scientists from U. Católica (DCC), U. Chile (CMM, DIE), U. Concepción (DCC), the Millennium Institute for Astrophysics – MAS and U. Nacional Andres Bello – UNAB (DCF) in Chile,  in collaboration with international researchers from Caltech (CD3), Harvard U. (IACS–SEAS) and U. of Washington (Dirac). In this talk I will discuss some data science challenges in astronomy and in particular for the problem of alert classification, including the ingestion, annotation, database access, processing power, machine learning classification and visualization of these alerts.