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Cloud-Native Repositories for Big Scientific Data
  • +9
  • Ryan Abernathey,
  • Tom Augspurger,
  • Anderson Banihirwe,
  • Charles C Blackmon-Luca,
  • Timothy J Crone,
  • Chelle L Gentemann,
  • Joseph J Hamman,
  • Naomi Henderson,
  • Chiara Lepore,
  • Theo A Mccaie,
  • Niall H Robinson,
  • Richard P Signell
Ryan Abernathey
Author Profile
Tom Augspurger
Anderson Banihirwe
Charles C Blackmon-Luca
Timothy J Crone
Chelle L Gentemann
Joseph J Hamman
Naomi Henderson
Chiara Lepore
Theo A Mccaie
Niall H Robinson
Richard P Signell


Scientific data has traditionally been distributed via the "download model," in which scientists bring datasets to the personal computers for analysis. But this way of working suffers from major limitations as scientific datasets grow towards the Petabyte scale. This article discusses the potential of cloud computing to accelerate scientific research and defines the concept of a "cloud native data repository," distinct from a traditional data repository. We enumerate the objectives for such repositories-performance, reliability, cost-effectiveness, collaboration, reproducibility, creativity, downstream impacts, and access & inclusion-and use these objectives to define a set of best practices for cloud native data repositories, focusing on the importance of analysis-ready data, cloud-optimized formats, and loose coupling with data-proximate computing. We describe a prototype implementation of these principles by the Pangeo Project using open source scientific python tools. We conclude by discussing some practical challenges for the future development of cloud native data repositories.

Peer review status:Published

03 Nov 2020Submitted to Computing in Science and Engineering
18 Jan 2021Published in Computing in Science and Engineering
01 Mar 2021Published in Computing in Science & Engineering volume 23 issue 2 on pages 26-35. 10.1109/MCSE.2021.3059437