Building A Community And Technology Stack For Scalable Big Data Geoscience At Pangeo

Summary

Science is founded on the collection and analysis of data. For disciplines that rely on data about the earth the ability to simulate and generate that data has been growing faster than the tools for analysis of that data can keep up with. In order to help scale that capacity for everyone working in geosciences the Pangeo project compiled a reference stack that combines powerful tools into an out-of-the-box solution for researchers to be productive in short order. In this episode Ryan Abernathy and Joe Hamman explain what the Pangeo project really is, how they have integrated a combination of XArray, Dask, and Jupyter to power these analytical workflows, and how it has helped to accelerate research on multidimensional geospatial datasets.

Announcements

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• Your host as usual is Tobias Macey and today I’m interviewing Ryan Abernathy and Joe Hamman about Pangeo, a community platform for Big Data geoscience

Interview

• Introductions
• How did you get introduced to Python?
• Can you describe what Pangeo is and the story behind it?
  • What is your role in the project/community and how did you get involved?
• What are the goals of the project and community?
  • What are the areas of effort and how are they organized?
• What are the scientific domains that Pangeo is focused on supporting?
  • What are the primary challenges associated with data management and analysis in these scientific communities?
• What are the forms that these data take and how have they been evolving? (e.g. formats/sources)
• What are some of the challenges introduced by the widespread adoption of cloud resources and the associated architectural patterns?
• Can you describe the technical components that fall under the Pangeo umbrella?
  • How do they come together to form a functional workflow for geo sciences?
• How has the scope of the Pangeo project changed or evolved since it started?
• What are the most interesting, innovative, or unexpected ways that you have seen Pangeo used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pangeo?
• When is Pangeo the wrong choice?
• What do you have planned for the future of Pangeo?

Keep In Touch

• Joe
  • @HammanHydro on Twitter
• Ryan
  • @rabernat on Twitter
  • rabernat on GitHub
  • Website

Picks

• Tobias
  • Mountain Biking
• Ryan
  • Klara And The Sun by Kazuo Ishiguro
• Joe
  • Range by David Epstein

Closing Announcements

• Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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Links

• Pangeo
• Pangeo Forge
• CarbonPlan
• M2LInES
• LEAP
• Columbia University
• XArray
• MIT
• MatLab
• PHP
• Ruby
• Java
• NumPy
• SciPy
• Matplotlib
• C
• Fortran
• Perl
• Dask
  • Data Engineering Podcast Episode
• Jupyter
• IDL
• HDF5
• Unidata
• NetCDF
• CF Metadata Conventions
• Intake
  • Podcast Episode
• FSSpec
• Parquet
  • Data Engineering Podcast Episode
• Zarr
• Data Engineering Podcast
• Pangeo Forge
• Airbyte
  • Data Engineering Podcast Episode
• Fivetran
  • Data Engineering Podcast Episode
• Stitch
• TileDB
  • Data Engineering Podcast Episode
• Pythia

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA