DOCS-modin-project#7287: Update Modin on Dask documentation

Signed-off-by: Kirill Suvorov <kirill.suvorov@intel.com>

Author: Retribution98

docs/development/using_pandas_on_dask.rst

@@ -20,4 +20,78 @@ or turn them on in source code:
 
    import modin.config as cfg
    cfg.Engine.put('dask')
-   cfg.StorageFormat.put('pandas')
+   cfg.StorageFormat.put('pandas')
+
+Using Modin on Dask locally
+---------------------------
+
+If you want to use a single node, just change the Modin Engine to Dask and 
+continue working with the Modin Dataframe as if it were a Pandas Dataframe.
+You don't even have to initialize the Dask Client, because Modin will do it 
+yourself or use the current one if it is already initialized:
+
+.. code-block:: python
+
+  import modin.pandas as pd
+  import modin.config as modin_cfg
+
+  modin_cfg.Engine.put("dask")
+  df = pd.read_parquet("s3://my-bucket/big.parquet")
+
+.. note:: In previous versions of Modin, you had to initialize Dask before importing Modin. As of Modin 0.9.0, This is no longer the case.
+
+Using Modin on Dask Clusters
+----------------------------
+
+If you want to use clusters of many machines, you don't need to do any additional steps.
+Just initialize a Dask Client on your cluster and use Modin as you would on a single node.
+As long as Dask Client is initialized before any dataframes are created, Modin
+will be able to connect to and use the Dask Cluster.
+
+.. code-block:: python
+
+  from distributed import Client
+  import modin.pandas as pd
+  import modin.config as modin_cfg
+  
+  # Please define your cluster here
+  cluster = ...
+  client = Client(cluster)
+
+  modin_cfg.Engine.put("dask")
+  df = pd.read_parquet("s3://my-bucket/big.parquet")
+
+To get more ways to deploy and run Dask clusters, visit the `Deploying Dask Clusters page`_.
+
+How Modin uses Dask
+-------------------
+
+Modin has a layered architecture, and the core abstraction for data manipulation
+is the Modin Dataframe, which implements a novel algebra that enables Modin to
+handle all of pandas (see Modin's documentation_ for more on the architecture).
+Modin's internal dataframe object has a scheduling layer that is able to partition
+and operate on data with Dask.
+
+Conversion to and from Modin from Dask Dataframe
+------------------------------------------------
+
+Modin DataFrame can be converted to/from Dask Dataframe with no-copy partition conversion.
+This allows you to take advantage of both Dask and Modin libraries for maximum performance.
+
+.. code-block:: python
+
+  import modin.pandas as pd
+  import modin.config as modin_cfg
+  from modin.pandas.io import to_dask, from_dask
+
+  modin_cfg.Engine.put("dask")
+  df = pd.read_parquet("s3://my-bucket/big.parquet")
+
+  # Convert Modin to Dask Dataframe
+  dask_df = to_dask(df)
+  
+  # Convert Dask to Modin Dataframe
+  modin_df = from_dask(dask_df)
+
+.. _Deploying Dask Clusters page: https://docs.dask.org/en/stable/deploying.html
+.. _documentation: https://modin.readthedocs.io/en/latest/development/architecture.html