fix: Fix NumPy - PyArrow array type mapping in Trino offline store (feast-dev#5393)

Author: ebolblga

docs/reference/data-sources/overview.md

@@ -28,4 +28,4 @@ Below is a matrix indicating which data sources support which types.
 | `float64`   | yes | yes | yes       | yes | yes | yes | yes | yes |
 | `bool`      | yes | yes | yes       | yes | yes | yes | yes | yes |
 | `timestamp` | yes | yes | yes       | yes | yes | yes | yes | yes |
-| array types | yes | yes | yes       | no  | yes | yes | no  | no  |
+| array types | yes | yes | yes       | no  | yes | yes | yes | no  |

docs/reference/data-sources/trino.md

@@ -8,7 +8,7 @@ These can be specified either by a table reference or a SQL query.
 ## Disclaimer
 
 The Trino data source does not achieve full test coverage.
-Please do not assume complete stability. 
+Please do not assume complete stability.
 
 ## Examples
 
@@ -30,5 +30,5 @@ The full set of configuration options is available [here](https://rtd.feast.dev/
 
 ## Supported Types
 
-Trino data sources support all eight primitive types, but currently do not support array types.
+Trino data sources support all eight primitive types and their corresponding array types.
 For a comparison against other batch data sources, please see [here](overview.md#functionality-matrix).

sdk/python/feast/infra/offline_stores/contrib/trino_offline_store/trino.py

@@ -207,9 +207,7 @@ def _to_df_internal(self, timeout: Optional[int] = None) -> pd.DataFrame:
 
     def _to_arrow_internal(self, timeout: Optional[int] = None) -> pyarrow.Table:
         """Return payrrow dataset as synchronously including on demand transforms"""
-        return pyarrow.Table.from_pandas(
-            self._to_df_internal(timeout=timeout), schema=self.pyarrow_schema
-        )
+        return pyarrow.Table.from_pandas(self._to_df_internal(timeout=timeout))
 
     def to_sql(self) -> str:
         """Returns the SQL query that will be executed in Trino to build the historical feature table"""