Cloud-native repositories for big scientific data
February 15, 2021
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.
|Cloud-native repositories for big scientific data
|Ryan Abernathey, 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
|Computing in Science and Engineering
|USGS Publications Warehouse
|Woods Hole Coastal and Marine Science Center