Add links to Xarray integration and blog post to docs (#243)

README.md

@@ -11,5 +11,6 @@ Cubed is a distributed N-dimensional array library implemented in Python using b
 - Follows [Dask Array](https://docs.dask.org/en/stable/array.html)'s chunked array API (`map_blocks`, `rechunk`, `apply_gufunc`, etc)
 - [Zarr](https://zarr.readthedocs.io/en/stable/) for persistent and intermediate storage
 - Multiple serverless runtimes: Python (in-process), [Lithops](https://lithops-cloud.github.io/), [Modal](https://modal.com/), [Apache Beam](https://beam.apache.org/)
+- Integration with [Xarray](https://xarray.dev/) via [cubed-xarray](https://github.com/xarray-contrib/cubed-xarray)
 
 [Documentation](https://tomwhite.github.io/cubed)

docs/index.md

@@ -9,6 +9,7 @@ Cubed is a distributed N-dimensional array library implemented in Python using b
 - Follows [Dask Array](https://docs.dask.org/en/stable/array.html)'s chunked array API (`map_blocks`, `rechunk`, etc)
 - [Zarr](https://zarr.readthedocs.io/en/stable/) for storage
 - Multiple serverless runtimes: Python (in-process), [Lithops](https://lithops-cloud.github.io/), [Modal](https://modal.com/), [Apache Beam](https://beam.apache.org/)
+- Integration with [Xarray](https://xarray.dev/) via [cubed-xarray](https://github.com/xarray-contrib/cubed-xarray)
 
 ## Documentation
 

docs/related-projects.md

@@ -10,6 +10,12 @@ Dask only has a single distributed runtime, Dask Distributed, whereas Cubed has
 
 The core operations and array API layers in Cubed are heavily influenced by Dask Array - in both naming, as well as implementation (Cubed uses Dask Array for some chunking utilities).
 
+## Xarray
+
+You can use Cubed with Xarray using the [cubed-xarray](https://github.com/xarray-contrib/cubed-xarray) package.
+
+Read more about the integration in [Cubed: Bounded-memory serverless array processing in xarray](https://xarray.dev/blog/cubed-xarray).
+
 ## Previous work
 
 This project is a continuation of a similar project I worked on, called [Zappy](https://github.com/lasersonlab/zappy/tree/master/zappy). What's changed in the intervening three years? Rechunker was created (I wasn't so concerned with memory when working on Zappy). The Python Array API standard has been created, which makes implementing a new array API less daunting than implementing the NumPy API. And I have a better understanding of Dask, and the fundamental nature of the blockwise operation.