Advancing ATLAS DCS Data Analysis with a Modern Data Platform
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866929684805058560 |
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| author | Canali, Luca Formica, Andrea Solis, Michelle Ann |
| author_facet | Canali, Luca Formica, Andrea Solis, Michelle Ann |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13543 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing ATLAS DCS Data Analysis with a Modern Data Platform Canali, Luca Formica, Andrea Solis, Michelle Ann Distributed, Parallel, and Cluster Computing High Energy Physics - Experiment Instrumentation and Detectors 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. |
| title | Advancing ATLAS DCS Data Analysis with a Modern Data Platform |
| topic | Distributed, Parallel, and Cluster Computing High Energy Physics - Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2501.13543 |