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Data Carpentry Beta This lesson is in the beta phase, which means that it is ready for teaching by instructors outside of the original author team.
    Data Carpentry
    Cloud-Native Geoscience Data Workflows
    • Cloud-Native Geoscience Data Workflows
    • Key Points
    • Instructor Notes
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      • Additional Exercises

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    Summary and Schedule
    1. Data Formats, Metadata, and Vocabulary
    2. Challenges of N-Dimensional Data
    3. Cloud-Native Formats
    4. Zarr Data Model and Chunked Storage
    5. Python for Zarr
    6. Choosing Chunks at Scale
    7. Parallel Processing with Zarr
    8. Reading Real-World Zarr Datasets in Python
    9. Object Storage and Cloud Data Organization
    10. Converting Traditional Formats to Zarr
    11. Case Studies
    12. Versioning Data with Icechunk
    13. Virtual Zarr with Virtualizarr
    14. Organizing Cloud Zarr Data with STAC
    15. Visualizing Multiscale Zarr and GeoZarr
    16. Architecture and Best Practices

    • Key Points
    • Instructor Notes
    • Extract All Images
    • Additional material
    • Additional Exercises

    • Discussion

    See all in one page

    Instructor Notes

    About the lesson


    Data Formats, Metadata, and Vocabulary


    Challenges of N-Dimensional Data


    Cloud-Native Formats


    Zarr Data Model and Chunked Storage


    Python for Zarr


    Choosing Chunks at Scale


    Parallel Processing with Zarr


    Reading Real-World Zarr Datasets in Python


    Object Storage and Cloud Data Organization


    Converting Traditional Formats to Zarr


    Case Studies


    Versioning Data with Icechunk


    Virtual Zarr with Virtualizarr


    Organizing Cloud Zarr Data with STAC


    Visualizing Multiscale Zarr and GeoZarr


    Architecture and Best Practices



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