Cloud-native repositories for big scientific data

Computing in Science and Engineering
By: , and 



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
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