diff --git a/cookbook/case_studies/feature_engineering/feast_integration/custom_provider/provider.py b/cookbook/case_studies/feature_engineering/feast_integration/custom_provider/provider.py
index 9cb1997016..a11d53a87e 100644
--- a/cookbook/case_studies/feature_engineering/feast_integration/custom_provider/provider.py
+++ b/cookbook/case_studies/feature_engineering/feast_integration/custom_provider/provider.py
@@ -20,30 +20,6 @@ class FlyteCustomProvider(LocalProvider):
     def __init__(self, config: RepoConfig, repo_path):
         super().__init__(config)
 
-    def materialize_single_feature_view(
-        self,
-        config: RepoConfig,
-        feature_view: FeatureView,
-        start_date: datetime,
-        end_date: datetime,
-        registry: Registry,
-        project: str,
-        tqdm_builder: Callable[[int], tqdm],
-    ) -> None:
-        # materialize_single_feature_view loads the latest feature values for a specific feature value from the offline
-        # store into the online store.
-        # This method can be overridden to also launch custom batch ingestion jobs that loads the latest batch feature
-        # values into the online store.
-        #
-
-        self._localize_feature_view(feature_view)
-
-        # Replace the line below with your custom logic in order to launch your own batch ingestion job
-        super().materialize_single_feature_view(
-            config, feature_view, start_date, end_date, registry, project, tqdm_builder
-        )
-        print("Launching custom batch jobs is pretty easy...")
-
     def get_historical_features(
         self,
         config: RepoConfig,
@@ -57,8 +33,21 @@ def get_historical_features(
         # get_historical_features returns a training dataframe from the offline store
 
         # We substitute the remote s3 file with a reference to a local file in each feature view being requested
-        for fv in feature_views:
-            self._localize_feature_view(fv)
+        for feature_view in feature_views:
+            if not isinstance(feature_view.batch_source, FileSource):
+                continue
+            # Copy parquet file to a local file
+            file_source: FileSource = feature_view.batch_source
+            random_local_path = FlyteContext.current_context().file_access.get_random_local_path(file_source.path)
+            FlyteContext.current_context().file_access.get_data(
+                file_source.path,
+                random_local_path,
+                is_multipart=True,
+            )
+            feature_view.batch_source=FileSource(
+                path=random_local_path,
+                event_timestamp_column=file_source.event_timestamp_column,
+            )
 
         return super().get_historical_features(
             config,
@@ -74,17 +63,3 @@ def _localize_feature_view(self, feature_view: FeatureView):
         """
         This function ensures that the `FeatureView` object points to files in the local disk
         """
-        if not isinstance(feature_view.batch_source, FileSource):
-            return
-        # Copy parquet file to a local file
-        file_source: FileSource = feature_view.batch_source
-        random_local_path = FlyteContext.current_context().file_access.get_random_local_path(file_source.path)
-        FlyteContext.current_context().file_access.get_data(
-            file_source.path,
-            random_local_path,
-            is_multipart=True,
-        )
-        feature_view.batch_source=FileSource(
-            path=random_local_path,
-            event_timestamp_column=file_source.event_timestamp_column,
-        )