Paper
10 July 2018 Framework to use modern big data software tools to improve operations at the Paranal Observatory
Author Affiliations +
Abstract
For almost two decades, large volumes of technical data, in a variety of formats, have resulted from the normal operations at the observatory. Similarly, in the last few years, dealing with huge amounts of data has become a priority for several industries, and as consequence, terms like "Big Data" or "Data Lake" have started to be more and more commonly used. Under these circumstances, frameworks and tools have proliferated and later released as "Open Software"; the hardware, on the other hand, has also changed giving the power to deal with this volume of data in a reasonable timeframe, and at a reasonable price.

We hereafter present the first version of a modern data lab developed for the Maintenance Support and Engineering Department (MSE) at the Paranal Observatory, “The MSE DataLab”. This DataLab will allow us to take advantage of this new technological evolution and to be prepared for the current and further challenges to come. These challenges, of course, refer to improving the overall observatory dependability (Reliability, Availability and Maintainability) by supporting the operations in our current and forthcoming telescopes. First, in our Very Large Telescopes (VLT), the VLT Interferometer (VLTI) and the survey telescopes (VISTA and VST). Secondly, in the Extremely Large Telescope (ELT) and the Cherenkov Telescope Array (CTA).
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eduardo Pena, Ricardo Schmutzer, Christian Stephan, Claudio Reinero, Julien Milli, Juan C. Guerra, and Juan Osorio "Framework to use modern big data software tools to improve operations at the Paranal Observatory", Proc. SPIE 10704, Observatory Operations: Strategies, Processes, and Systems VII, 107042J (10 July 2018); https://doi.org/10.1117/12.2312096
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Databases

Observatories

Machine learning

Data storage

Computing systems

Software engineering

Distributed computing

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