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For over 20 years, ESIP meetings have brought together the most innovative thinkers and leaders around Earth observation data, thus forming a community dedicated to making Earth observations more discoverable, accessible and useful to researchers, practitioners, policy makers, and the public. The theme of the meeting is Putting Data to Work: Building Public-Private Partnerships to Increase Resilience & Enhance the Socioeconomic Value of Data.

The meeting has now ended. Check out the ESIP Summer Meeting Highlights Webinar and learn how to access session materials at https://www.esipfed.org/collaboration-updates/esip-summer-meeting-2020-recap.
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Wednesday, July 22 • 11:30am - 1:00pm
HDF Town Hall

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HDF5 is one of the most widely used file formats for Earth Science data. One of the strategic goals of The HDF Group is to enable seamless transition to cloud computing for all applications depending on the HDF5 API. This session will provide several use cases of HDF Group's own and third-party solutions on how to access and analyze HDF5 data in cloud object stores. Also included will be an update on the HDF5 library's road map with emphasis on the new feature in the latest version 1.12.0 which enables access to different file formats and backend storage systems through HDF5 API.

Agenda
  1. Elena Pourmal (HDF Group): HDF5 and Ecosystem: What Is New? 
  2. Michael Rilee (Rilee Systems Technologies), Kwo-Sen Kuo (Bayesics LLC), James Gallagher (OPeNDAP), James Frew (UC Santa Barbara), Niklas Griessbaum (UC Santa Barbara), Edward Hartnett (Ed Hartnett Consulting), Robert Wolfe (NASA Goddard Space Flight Center), Gerd Heber (The HDF Group), Siri Jodha Khalsa (CASL LLC): STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer for Compatibility
  3. H. Joe Lee (HDF Group): Apache Drill/Unidata TDS for NASA HDF-EOS on S3
  4. John Readey (HDF Group): HDF for the Cloud - New HDF Server Features
  5. Aleksandar Jelenak (HDF Group): HDF5 ↔︎ Zarr
View Recording
View Session Notes
View Presentations: See attached.

Takeaways
  • Breaking large data volumes into small objects provides the benefits of 1) small data replacements on update, 2) greater opportunity for parallelism on a single data access, 3) mitigation of memory requirements on a server involved in reads and writes.





Wednesday July 22, 2020 11:30am - 1:00pm EDT
Room 3