Cloud-native repositories for big scientific data
Computing in Science and Engineering
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Abstract
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.
Suggested Citation
Abernathey, R., Augspurger, T., Banihirwe, A., Blackmon-Luca, C.C., Crone, T., Gentemann, C., Hamman, J., Henderson, N., Lepore, C., McCaie, T., Robinson, N., Signell, R.P., 2021, Cloud-native repositories for big scientific data: Computing in Science and Engineering, v. 23, no. 2, p. 26-35, https://doi.org/10.1109/MCSE.2021.3059437.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Cloud-native repositories for big scientific data |
| Series title | Computing in Science and Engineering |
| DOI | 10.1109/MCSE.2021.3059437 |
| Volume | 23 |
| Issue | 2 |
| Year Published | 2021 |
| Language | English |
| Publisher | IEEE |
| Contributing office(s) | Woods Hole Coastal and Marine Science Center |
| Description | 10 p. |
| First page | 26 |
| Last page | 35 |