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TY - CONF AU - Costa, A. AU - Becciani, U. AU - Bruno, P. AU - Calanducci, A.S. AU - Conforti, V. AU - Gianotti, F. AU - Grillo, A. AU - Riggi, S. AU - Schwarz, J. AU - Sciacca, E. AU - Tosti, G. AU - Vitello, F. ED - White, Karen S. ED - Brown, Kevin A. ED - Dyer, Philip S. ED - Schaa, Volker RW TI - Big Data Architectures for Logging and Monitoring Large Scale Telescope Arrays J2 - Proc. of ICALEPCS2019, New York, NY, USA, 05-11 October 2019 CY - New York, NY, USA T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 17 LA - english AB - Large volumes of technical and logging data result from the operation of large scale astrophysical infrastructures. In the last few years several "Big Data" technologies have been developed to deal with a huge amount of data, e.g. in the Internet of Things (IoT) framework. We are comparing different stacks of Big Data/IoT architectures including high performance distributed messaging systems, time series databases, streaming systems, interactive data visualization. The main aim is to classify these technologies based on a set of use cases typically related to the data produced in the astronomical environment, with the objective to have a system that can be updated, maintained and customized with a minimal programming effort. We present the preliminary results obtained, using different Big Data stack solution to manage some use cases related to quasi real-time collection, processing and storage of the technical data, logging and technical alert produced by the array of nine ASTRI telescopes that are under development by INAF as a pathfinder array for the Cherenkov astronomy in the TeV energy range. PB - JACoW Publishing CP - Geneva, Switzerland SP - 268 EP - 271 KW - software KW - monitoring KW - controls KW - operation KW - database DA - 2020/08 PY - 2020 SN - 2226-0358 SN - 978-3-95450-209-7 DO - doi:10.18429/JACoW-ICALEPCS2019-MOPHA032 UR - https://jacow.org/icalepcs2019/papers/mopha032.pdf ER -