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.
| 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 |
| Google Analytic Metrics | Metrics page |