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Showing content from https://github.com/zarr-developers/VirtualiZarr below:

zarr-developers/VirtualiZarr: Cloud-Optimize your Scientific Data as Virtual Zarr stores, using xarray syntax.

The best way to distribute large scientific datasets is via the Cloud, in Cloud-Optimized formats ¹. But often this data is stuck in archival pre-Cloud file formats such as netCDF.

VirtualiZarr² makes it easy to create "Virtual" Zarr datacubes, allowing performant access to archival data as if it were in the Cloud-Optimized Zarr format, without duplicating any data.

Please see the documentation.

• Create virtual references pointing to bytes inside an archival file with open_virtual_dataset.
• Supports a range of archival file formats, including netCDF4 and HDF5, and has a pluggable system for supporting new formats.
• Access data via the zarr-python API by reading from the zarr-compatible ManifestStore.
• Combine data from multiple files into one larger datacube using xarray's combining functions, such as xarray.concat.
• Commit the virtual references to storage either using the Kerchunk references specification or the Icechunk transactional storage engine.
• Users access the virtual datacube simply as a single zarr-compatible store using xarray.open_zarr.

VirtualiZarr grew out of discussions on the Kerchunk repository, and is an attempt to provide the game-changing power of kerchunk but in a zarr-native way, and with a familiar array-like API.

You now have a choice between using VirtualiZarr and Kerchunk: VirtualiZarr provides almost all the same features as Kerchunk.

VirtualiZarr version 1 (mostly) achieved feature parity with kerchunk's logic for combining datasets, providing an easier way to manipulate kerchunk references in memory and generate kerchunk reference files on disk.

VirtualiZarr version 2 brings:

• Zarr v3 support
• A pluggable system of "parsers" for virtualizing custom file formats
• The ManifestStore abstraction, which allows for loading data without serializing to Kerchunk/Icechunk first
• Integration with obstore
• Reference parsing that doesn't rely on kerchunk under the hood
• The ability to use "parsers" to load data directly from archival file formats into Zarr and/or Xarray

Future VirtualiZarr development will focus on generalizing and upstreaming useful concepts into the Zarr specification, the Zarr-Python library, Xarray, and possibly some new packages.

We have a lot of ideas, including:

• Zarr-native on-disk chunk manifest format
• "Virtual concatenation" of separate Zarr arrays
• ManifestArrays as an intermediate layer in-memory in Zarr-Python
• Separating CF-related Codecs from xarray

If you see other opportunities then we would love to hear your ideas!

• 2025/04/30 - Cloud-Native Geospatial Forum - Tom Nicholas - Slides / Recording
• 2024/11/21 - MET Office Architecture Guild - Tom Nicholas - Slides
• 2024/11/13 - Cloud-Native Geospatial conference - Raphael Hagen - Slides
• 2024/07/24 - ESIP Meeting - Sean Harkins - Event / Recording
• 2024/05/15 - Pangeo showcase - Tom Nicholas - Event / Recording / Slides

This package was originally developed by Tom Nicholas whilst working at [C]Worthy, who deserve credit for allowing him to prioritise a generalizable open-source solution to the dataset virtualization problem. VirtualiZarr is now a community-owned multi-stakeholder project.

Apache 2.0

1. Cloud-Native Repositories for Big Scientific Data, Abernathey et. al., Computing in Science & Engineering. ↩

2. (Pronounced "Virtual-Eye-Zarr" - like "virtualizer" but more piratey 🦜) ↩

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