This is a bit off-topic but I'm interested in the same space you are in.
There seems to be an inherent pull between large chunks (great for visualising large extents and larger queries) vs smaller chunks for point-based or timeseries queries. It's possible but not very cost-effective to maintain separately-chunked versions of these large geospatial datasets. I have heard of "kerchunk" being used to try and get the best of both, but then I _think_ you lose out on the option of compressing the data and it introduces quite a lot of complexity.
What are your thoughts on how to strike that balance between use cases?
There seems to be an inherent pull between large chunks (great for visualising large extents and larger queries) vs smaller chunks for point-based or timeseries queries. It's possible but not very cost-effective to maintain separately-chunked versions of these large geospatial datasets. I have heard of "kerchunk" being used to try and get the best of both, but then I _think_ you lose out on the option of compressing the data and it introduces quite a lot of complexity.
What are your thoughts on how to strike that balance between use cases?