The last 2 years of my professional life have in some sense all been working towards this blog post.
It shows how you can make an enormous archive of public scientific data[1] much easier to use for everyone, without having to copy all the data, which would be prohibitively expensive at this scale.
I also made some sweet gifs and images of the Earth from space from the raw data so check those out!
[1] In this case satellite data, but this could work for data from many other fields - my background is original nuclear fusion plasma physics then oceanography.
Awesome to see this project here - it was partly inspired by my blog post (original is linked from the OP, but there's a slightly newer version on my personal site here[0]).
What you describe is very similar to how Icechunk[1] works. It works beautifully for transactional writes to "repos" containing PBs of scientific array data in object storage.
People have literally used Zarr for this - at one point Gemini used Zarr for checkpointing model weights. Not sure what the current fashion in that space is though.
It's definitely one of many fields that see convergent evolution towards something that just looks like Zarr. In fact you can use VirtualiZarr to parse HuggingFace's "SafeTensors" format [0].
IMO Zarr is that newer format. It abstracts over the features of all these other formats so neatly that it can literally subsume them.
I feel that we no longer really need TIFF etc. - for scientific use cases in the cloud Zarr is all that's needed going forwards. The other file formats become just archival blobs that either are converted to Zarr or pointed at by virtual Zarr stores.
The generalized form of this range-request-based streaming approach looks something like my project VirtualiZarr [0].
Many of these scientific file formats (HDF5, netCDF, TIFF/COG, FITS, GRIB, JPEG and more) are essentially just contiguous multidimensional array(/"tensor") chunks embedded alongside metadata about what's in the chunks. Efficiently fetching these from object storage is just about efficiently fetching the metadata up front so you know where the chunks you want are [1].
The data model of Zarr [2] generalizes this pattern pretty well, so that when backed by Icechunk [3], you can store a "datacube" of "virtual chunk references" that point at chunks anywhere inside the original files on S3.
This allows you to stream data out as fast as the S3 network connection allows [4], and then you're free to pull that directly, or build tile servers on top of it [5].
In the Pangeo project and at Earthmover we do all this for Weather and Climate science data. But the underlying OSS stack is domain-agnostic, so works for all sorts of multidimensional array data, and VirtualiZarr has a plugin system for parsing different scientific file formats.
I would love to see if someone could create a virtual Zarr store pointing at this WSI data!
God this article is 10000% better than the posted one. This is great:
> Names should not describe what you currently think the thing you’re naming is for. Imagine naming your newborn child "Doctor", or "SupportsMeInMyOldAge". Poor kid.
I would love to bring these benefits to the multidimensional array world, via integration with the Zarr/Icechunk formats somehow (which I work on). But this fragmentation of formats makes it very hard to know where to start.
The pitch for this sounds very similar to the pitch for Vortex (i.e. obviating the need to create a new format every time a shift occurs in data processing and computing by providing a data organization structure and a general-purpose API to allow developers to add new encoding schemes easily).
But I'm not totally clear what the relationship between F3 and Vortex is. It says their prototype uses the encoding implementation in Vortex, but does not use the Vortex type system?
It shows how you can make an enormous archive of public scientific data[1] much easier to use for everyone, without having to copy all the data, which would be prohibitively expensive at this scale.
I also made some sweet gifs and images of the Earth from space from the raw data so check those out!
[1] In this case satellite data, but this could work for data from many other fields - my background is original nuclear fusion plasma physics then oceanography.