| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01110 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701110 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701110
Advancing ATLAS Detector Control System Data Analysis with a Modern Data Platform
1 CERN, CH-1211 Geneva 23 (Switzerland)
2 IRFU, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette (France)
3 University of Arizona, (USA)
* e-mail: luca.canali@cern.ch
** e-mail: andrea.formica@cern.ch
*** e-mail: msolis@arizona.edu
Published online: 7 October 2025
This paper presents a modern and scalable framework for analyzing Detector Control System (DCS) data from the ATLAS experiment at CERN. The DCS data, stored in an Oracle database via the WinCC OA system, is optimized for transactional operations, posing challenges for large-scale analysis across extensive time periods and devices. To address these limitations, we developed a data pipeline using Apache Spark, CERN’s Hadoop service, and the CERN SWAN platform. This framework integrates seamlessly with Python notebooks, providing an accessible and efficient environment for data analysis using industry-standard tools. The approach has proven effective in troubleshooting Data Acquisition (DAQ) links for the ATLAS New Small Wheel (NSW) detector, demonstrating the value of modern data platforms in enabling detector experts to quickly identify and resolve critical issues.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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