CTAARCHS: Cloud-based Technologies for Archival Astronomical Research Contents and Handling Systems

This paper presents a flexible approach to a multipurpose, heterogeneous archive model that merges the robustness of legacy Grid-based technologies with modern Cloud and Edge computing paradigms. It leverages innovations driven by Big Data, IoT, AI, and Machine Learning to create an adaptive data storage and processing framework. In today’s digital age, where data is the new intangible gold, the “gold rush” lies in managing and storing massive datasets effectively—especially when these data serve governmental or commercial purposes, raising concerns about privacy and the misuse by third-party aggregators. Astronomical data, in particular, require this same thoughtful approach. Scientific discovery increasingly depends on efficient extraction and processing of large datasets. Distributed archival models, unlike centralized warehouses, offer scalability by allowing data to be accessed and processed across locations via cloud services. Incorporating edge computing further enables real-time access with reduced latency. Major astronomical projects must also avoid common Single Points of Failure (SPOFs), often resulting from suboptimal technological choices driven by collaboration politics or In-Kind Contributions (IKCs). These missteps can hinder innovation and long-term project success. This paper outlines best practices in archive project management—from policy development and task planning to use-case definition and implementation. Only after these steps can a coherent selection of hardware, software, or virtual environments be made. The proposed model—CTAARCHS (Cloud-based Technologies for Astronomical Archiving Research Contents & Handling Systems)—is an open-source, multidisciplinary platform supporting big data needs in astronomy. It promotes broad institutional collaboration, offering code repositories and sample data for immediate use.