Tom Augspurger Anderson Banihirwe Charles C. Blackmon-Luca Timothy Crone Chelle Gentemann Joseph Hamman Naomi Henderson Chiara Lepore Theo McCaie Niall Robinson Richard P. Signell Ryan Abernathey 2021 <div class="abstract-text row"><div class="col-12"><div class="u-mb-1"><div>Scientific data have traditionally been distributed via downloads from data server to local computer. This way of working suffers from limitations as scientific datasets grow toward the petabyte scale. A “cloud-native data repository,” as defined in this article, offers several advantages over traditional data repositories—performance, reliability, cost-effectiveness, collaboration, reproducibility, creativity, downstream impacts, and access and inclusion. These objectives motivate a set of best practices for cloud-native data repositories: analysis-ready data, cloud-optimized (ARCO) formats, and loose coupling with data-proximate computing. The Pangeo Project has developed a prototype implementation of these principles by using open-source scientific Python tools. By providing an ARCO data catalog together with on-demand, scalable distributed computing, Pangeo enables users to process big data at rates exceeding 10 GB/s. Several challenges must be resolved in order to realize cloud computing’s full potential for scientific research, such as organizing funding, training users, and enforcing data privacy requirements.</div></div></div></div> application/pdf 10.1109/MCSE.2021.3059437 en IEEE Cloud-native repositories for big scientific data article