DOCS-#3776: Improve documentation for pandas_on_dask execution (#3785)

Signed-off-by: Alexey Prutskov <alexey.prutskov@intel.com>

docs/flow/modin/core/execution/dask/implementations/pandas_on_dask/index.rst

@@ -1,10 +1,36 @@
 :orphan:
 
+PandasOnDask Execution
+======================
+
+Queries that perform data transformation, data ingress or data egress using the `pandas on Dask` execution
+pass through the Modin components detailed below.
+
+To enable `pandas on Dask` execution, please refer to the usage section in :doc:`pandas on Dask </developer/using_pandas_on_dask>`.
+
+Data Transformation
+'''''''''''''''''''
+
+.. image:: /img/pandas_on_dask_data_transform.svg
+   :align: center
+
+When a user calls any :py:class:`~modin.pandas.dataframe.DataFrame` API, a query starts forming at the `API` layer
+to be executed at the `Execution` layer. The `API` layer is responsible for processing the query appropriately,
+for example, determining whether the final result should be a ``DataFrame`` or ``Series`` object. This layer is also responsible for sanitizing the input to the
+:py:class:`~modin.core.storage_formats.pandas.query_compiler.PandasQueryCompiler`, e.g. validating a parameter from the query
+and defining specific intermediate values to provide more context to the query compiler.
+The :py:class:`~modin.core.storage_formats.pandas.query_compiler.PandasQueryCompiler` is responsible for
+processing the query, received from the :py:class:`~modin.pandas.dataframe.DataFrame` `API` layer,
+to determine how to apply it to a subset of the data - either cell-wise or along an axis-wise partition backed by the `pandas`
+storage format. The :py:class:`~modin.core.storage_formats.pandas.query_compiler.PandasQueryCompiler` maps the query to one of the :doc:`Core Algebra Operators </flow/modin/core/dataframe/algebra>` of
+the :py:class:`~modin.core.execution.dask.implementations.pandas_on_dask.dataframe.dataframe.PandasOnDaskDataframe` which inherits
+generic functionality from the :py:class:`~modin.core.dataframe.pandas.dataframe.dataframe.PandasDataframe`.
+
 PandasOnDask Dataframe implementation
-=====================================
+-------------------------------------
 
-This page describes the implementation of :doc:`Modin PandasDataframe Objects </flow/modin/core/dataframe/pandas/index>`
-specific for `PandasOnDask` execution.
+Modin implements ``Dataframe``, ``PartitionManager``, ``AxisPartition`` and ``Partition`` classes
+specifically for the `PandasOnDask` execution.
 
 * :doc:`PandasOnDaskDataframe <dataframe>`
 * :doc:`PandasOnDaskDataframePartition <partitioning/partition>`
@@ -18,3 +44,40 @@ specific for `PandasOnDask` execution.
     partitioning/partition
     partitioning/axis_partition
     partitioning/partition_manager
+
+
+Data Ingress
+''''''''''''
+
+.. image:: /img/pandas_on_dask_data_ingress.svg
+   :align: center
+
+Data Egress
+'''''''''''
+
+.. image:: /img/pandas_on_dask_data_egress.svg
+   :align: center
+
+
+When a user calls any IO function from the ``modin.pandas.io`` module, the `API` layer queries the
+:py:class:`~modin.core.execution.dispatching.factories.dispatcher.FactoryDispatcher` which defines a factory specific for
+the execution, namely, the :py:class:`~modin.core.execution.dispatching.factories.factories.PandasOnDaskFactory`. The factory, in turn,
+exposes the :py:class:`~modin.core.execution.dask.implementations.pandas_on_dask.io.PandasOnDaskIO` class
+whose responsibility is to perform a parallel read/write from/to a file.
+
+When reading data from a CSV file, for example, the :py:class:`~modin.core.execution.dask.implementations.pandas_on_dask.io.io.PandasOnDaskIO` class forwards
+the user query to the :meth:`~modin.core.io.text.CSVDispatcher._read` method of :py:class:`~modin.core.io.text.CSVDispatcher`, where the query's parameters are preprocessed
+to check if they are supported by the execution (defaulting to pandas if they are not) and computes some metadata
+common for all partitions to be read. Then, the file is split into row chunks, and this data is used to launch remote tasks on the Dask workers
+via the :meth:`~modin.core.execution.dask.common.task_wrapper.DaskTask.deploy` method of :py:class:`~modin.core.execution.dask.common.task_wrapper.DaskTask`.
+On each Dask worker, the :py:class:`~modin.core.storage_formats.pandas.parsers.PandasCSVParser` parses data.
+After the remote tasks are finished, additional result postprocessing is performed,
+and a new query compiler with the data read is returned.
+
+When writing data to a CSV file, for example, the :py:class:`~modin.core.execution.dask.implementations.pandas_on_dask.io.PandasOnDaskIO` processes
+the user query to execute it on Dask workers. Then, the :py:class:`~modin.core.execution.dask.implementations.pandas_on_dask.io.PandasOnDaskIO` asks the
+:py:class:`~modin.core.execution.dask.implementations.pandas_on_dask.io.PandasOnDaskDataframe` to decompose the data into row-wise partitions
+that will be written into the file in parallel in Dask workers.
+
+.. note::
+   Currently, data egress uses default `pandas` implementation for `pandas on Dask` execution.