updated docstring for ManagedTableDataSet

Signed-off-by: Danny Farah <danny_farah@mckinsey.com>

kedro-datasets/kedro_datasets/databricks/managed_table_dataset.py

@@ -148,10 +148,18 @@ def schema(self) -> StructType:
 
 class ManagedTableDataSet(AbstractVersionedDataSet):
     """``ManagedTableDataSet`` loads and saves data into managed delta tables on Databricks.
+        Load and save can be in Spark or Pandas dataframes, specified in dataframe_type.
+        When saving data, you can specify one of three modes: overwtire(default), append,
+        or upsert. Upsert requires you to specify the primary_column parameter which
+        will be used as part of the join condition. This dataset works best with
+        the databricks kedro starter. That starter comes with hooks that allow this
+        dataset to function properly. Follow the instructions in that starter to
+        setup your project for this dataset.
 
         Example usage for the
         `YAML API <https://kedro.readthedocs.io/en/stable/data/\
         data_catalog.html#use-the-data-catalog-with-the-yaml-api>`_:
+
         .. code-block:: yaml
 
             names_and_ages@spark:
@@ -167,23 +175,24 @@ class ManagedTableDataSet(AbstractVersionedDataSet):
         `Python API <https://kedro.readthedocs.io/en/stable/data/\
         data_catalog.html#use-the-data-catalog-with-the-code-api>`_:
         ::
-        Launch a pyspark session with the following configs:
-        % pyspark --packages io.delta:delta-core_2.12:1.2.1
-        --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension"
-        --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"
-
-        >>> from pyspark.sql import SparkSession
-        >>> from pyspark.sql.types import (StructField, StringType,
-                                        IntegerType, StructType)
-        >>> from kedro_datasets.databricks import ManagedTableDataSet
-        >>> schema = StructType([StructField("name", StringType(), True),
-                                 StructField("age", IntegerType(), True)])
-        >>> data = [('Alex', 31), ('Bob', 12), ('Clarke', 65), ('Dave', 29)]
-        >>> spark_df = SparkSession.builder.getOrCreate().createDataFrame(data, schema)
-        >>> data_set = ManagedTableDataSet(table="names_and_ages")
-        >>> data_set.save(spark_df)
-        >>> reloaded = data_set.load()
-        >>> reloaded.take(4)"""
+
+            % pyspark --packages io.delta:delta-core_2.12:1.2.1
+            --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension"
+            --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"
+
+            >>> from pyspark.sql import SparkSession
+            >>> from pyspark.sql.types import (StructField, StringType,
+                                            IntegerType, StructType)
+            >>> from kedro_datasets.databricks import ManagedTableDataSet
+            >>> schema = StructType([StructField("name", StringType(), True),
+                                    StructField("age", IntegerType(), True)])
+            >>> data = [('Alex', 31), ('Bob', 12), ('Clarke', 65), ('Dave', 29)]
+            >>> spark_df = SparkSession.builder.getOrCreate().createDataFrame(data, schema)
+            >>> data_set = ManagedTableDataSet(table="names_and_ages")
+            >>> data_set.save(spark_df)
+            >>> reloaded = data_set.load()
+            >>> reloaded.take(4)
+        """
 
     # this dataset cannot be used with ``ParallelRunner``,
     # therefore it has the attribute ``_SINGLE_PROCESS = True``
@@ -207,7 +216,36 @@ def __init__(  # pylint: disable=R0913
         partition_columns: List[str] = None,
         owner_group: str = None,
     ) -> None:
-        """Creates a new instance of ``ManagedTableDataSet``."""
+        """Creates a new instance of ``ManagedTableDataSet``
+
+        Args:
+            table (str): the name of the table
+            catalog (str, optional): the name of the catalog in Unity.
+             Defaults to None.
+            database (str, optional): the name of the database
+             (also referred to as schema). Defaults to "default".
+            write_mode (str, optional): the mode to write the data into the table.
+             Options are:["overwrite", "append", "upsert"].
+             "upsert" mode requires primary_key field to be populated.
+             Defaults to "overwrite".
+            dataframe_type (str, optional): "pandas" or "spark" dataframe.
+             Defaults to "spark".
+            primary_key (Union[str, List[str]], optional): the primary key of the table.
+             Can be in the form of a list. Defaults to None.
+            version (Version, optional): kedro.io.core.Version instance to load the data.
+             Defaults to None.
+            schema (Dict[str, Any], optional): the schema of the table in JSON form.
+             Dataframes will be truncated to match the schema if provided.
+             Used by the hooks to create the table if the schema is provided
+             Defaults to None.
+            partition_columns (List[str], optional): the columns to use for partitioning the table.
+             Used by the hooks. Defaults to None.
+            owner_group (str, optional): if table access control is enabled in your workspace,
+             specifying owner_group will transfer ownership of the table and database to
+             this owner. All databases should have the same owner_group. Defaults to None.
+        Raises:
+            DataSetError: Invalid configuration supplied (through ManagedTable validation)
+        """
 
         self._table = ManagedTable(
             database=database,