xarray-contrib
diff --git a/‎advanced/backends/backends.md
Lines changed: 7 additions & 65 deletions b/‎advanced/backends/backends.md
Lines changed: 7 additions & 65 deletions
@@ -9,72 +9,14 @@ import xarray as xr
 xr.open_dataset("my_file.grib" , engine="cfgrib")
 ```
 
-The "engine" provides a set of instructions that tells xarray how to read the data and pack them into a `dataset` (or `dataarray`).
-These instructions are stored in an underlying "backend".
+Navigating Xarray backends can be confusing,
+so we recommend checking out [this flow chart](https://docs.xarray.dev/en/stable/user-guide/io.html)
+to help you figure out which engine you need and how to use it.
 
-Xarray comes with several backends that cover many common data formats.
-Many more backends are available via external libraries, or you can write your own.
-This diagram aims to help you determine - based on the format of the file you'd like to read -
-which type of backend you're using and how to use it.
-The rectangular boxes are clickable with more information.
+You can see what backends are currently available in your working environment
+with `xarray.backends.list_engines()`.
 
-```{mermaid}
----
-title: "Xarray Engines: Opening Your Data with xr.open_dataset()"
----
-
-
-flowchart TD
-    built-in-eng[Is your data stored in one of these formats?
-        netCDF4 (**netcdf4**)
-        netCDF3 (**scipy**)
-        Zarr (**zarr**)
-        DODS/OPeNDAP (**pydap**)
-        HDF5 (**h5netcdf**)
-        ]
-
-    built-in("`You're in luck! Xarray bundles a backend for this format.
-        Open data using *xr.open_dataset()*. We recommend
-        always setting the engine you want to use.`")
-
-    installed-eng["`One of these formats (this list is not inclusive)?
-        GRIB (**cfgrib**)
-        TileDB (**tiledb**)
-        GeoTIFF, JPEG-2000, ESRI-hdf (**rioxarray**, via GDAL)
-        Sentinel-1 SAFE (**xarray-sentinel**)
-        ...
-        `"]
-
-    installed("`Install the package indicated in parentheses
-        to your Python environment. Restart the kernel
-        and use *xr.open_dataset(files, engine='rioxarray')*`")
-
-    other("`Ask around to see if someone in your data community
-        has created an Xarray backend for your data type.
-        If not, you may need to create your own or consider
-        exporting your data to a more common format.`")
-
-    built-in-eng -->|Yes| built-in
-    built-in-eng -->|No| installed-eng
-
-    installed-eng -->|Yes| installed
-    installed-eng -->|No| other
-
-    click built-in-eng "https://docs.xarray.dev/en/stable/getting-started-guide/faq.html#how-do-i-open-format-x-file-as-an-xarray-dataset"
-    click installed-eng "https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html#rioxarray"
-    click other "https://docs.xarray.dev/en/stable/internals/how-to-add-new-backend.html"
-
-    classDef quesNodefmt fill:#9DEEF4,stroke:#206C89
-    class built-in-eng,installed-eng quesNodefmt
-
-    classDef ansNodefmt fill:#FFAA05,stroke:#E37F17
-    class built-in,installed,other ansNodefmt
-
-    linkStyle default font-size:20pt,color:#206C89
-
-```
-
-## Why use the Xarray backend API?
+## Why use the Xarray backend API to write your own backend?
 
 - Your users don't need to learn a new interface; they can use `xr.open_dataset` with the `engine` kwarg.
 - With little extra effort you can have lazy loading with Dask. Simply implement a function for reading blocks and Xarray will manage lazy loading with Dask for you.
@@ -93,7 +35,7 @@ Follow the tutorials on creating a new backend for binary files.
 ### Links to internal backends
 
 - [netcdf4](https://pypi.org/project/netCDF4/) - netCDF4
-- [scipy]https://scipy.org/) - netCDF3
+- [scipy](https://scipy.org/) - netCDF3
 - [zarr](https://pypi.org/project/zarr/) - Zarr
 - [pydap](https://pydap.github.io/pydap/) - Data Access Protocol (DAP/DODS/OPeNDAP)