diff --git a/sdk/python/feast/feature_store.py b/sdk/python/feast/feature_store.py
index 0fa2ba855d..cd669f1a33 100644
--- a/sdk/python/feast/feature_store.py
+++ b/sdk/python/feast/feature_store.py
@@ -1498,12 +1498,14 @@ def write_to_online_store(
             allow_registry_cache (optional): Whether to allow retrieving feature views from a cached registry.
         """
         # TODO: restrict this to work with online StreamFeatureViews and validate the FeatureView type
+        # In EG, we use feature view to support streaming and http registry is not implemented for stream feature views
+        # HTTP Registry raises MethodNotImplementedError for get_stream_feature_view
         try:
-            feature_view = self.get_stream_feature_view(
+            feature_view = self.get_feature_view(
                 feature_view_name, allow_registry_cache=allow_registry_cache
             )
         except FeatureViewNotFoundException:
-            feature_view = self.get_feature_view(
+            feature_view = self.get_stream_feature_view(
                 feature_view_name, allow_registry_cache=allow_registry_cache
             )
         provider = self._get_provider()
@@ -1663,9 +1665,9 @@ def _get_online_features(
 
             return self._go_server.get_online_features(
                 features_refs=features if isinstance(features, list) else [],
-                feature_service=features
-                if isinstance(features, FeatureService)
-                else None,
+                feature_service=(
+                    features if isinstance(features, FeatureService) else None
+                ),
                 entities=entity_native_values,
                 request_data={},  # TODO: add request data parameter to public API
                 full_feature_names=full_feature_names,
@@ -2093,9 +2095,11 @@ def _populate_response_from_feature_data(
         """
         # Add the feature names to the response.
         requested_feature_refs = [
-            f"{table.projection.name_to_use()}__{feature_name}"
-            if full_feature_names
-            else feature_name
+            (
+                f"{table.projection.name_to_use()}__{feature_name}"
+                if full_feature_names
+                else feature_name
+            )
             for feature_name in requested_features
         ]
         online_features_response.metadata.feature_names.val.extend(
diff --git a/sdk/python/feast/infra/contrib/spark_kafka_processor.py b/sdk/python/feast/infra/contrib/spark_kafka_processor.py
index f8295cdcf5..aea0434d59 100644
--- a/sdk/python/feast/infra/contrib/spark_kafka_processor.py
+++ b/sdk/python/feast/infra/contrib/spark_kafka_processor.py
@@ -182,6 +182,11 @@ def _construct_transformation_plan(self, df: StreamTable) -> StreamTable:
         if isinstance(self.sfv, FeatureView):
             drop_list: List[str] = []
             fv_schema: Set[str] = set(map(lambda field: field.name, self.sfv.schema))
+            # Add timestamp field to the schema so we don't delete from dataframe
+            if isinstance(self.sfv, StreamFeatureView):
+                fv_schema.add(self.sfv.timestamp_field)
+            else:
+                fv_schema.add(self.sfv.stream_source.timestamp_field)
             for column in df.columns:
                 if column not in fv_schema:
                     drop_list.append(column)
diff --git a/sdk/python/feast/infra/materialization/contrib/spark/spark_materialization_engine.py b/sdk/python/feast/infra/materialization/contrib/spark/spark_materialization_engine.py
index ed4388aeb3..0f9a668b12 100644
--- a/sdk/python/feast/infra/materialization/contrib/spark/spark_materialization_engine.py
+++ b/sdk/python/feast/infra/materialization/contrib/spark/spark_materialization_engine.py
@@ -1,3 +1,4 @@
+import logging
 from dataclasses import dataclass
 from datetime import datetime
 from typing import Callable, List, Literal, Optional, Sequence, Union, cast
@@ -32,6 +33,8 @@
     _run_pyarrow_field_mapping,
 )
 
+logger = logging.getLogger(__name__)
+
 
 class SparkMaterializationEngineConfig(FeastConfigBaseModel):
     """Batch Materialization Engine config for spark engine"""
@@ -235,7 +238,7 @@ def _process_by_partition(rows, spark_serialized_artifacts: _SparkSerializedArti
 
     df = pd.DataFrame.from_records(dicts)
     if df.shape[0] == 0:
-        print("Skipping")
+        logger.info("Dataframe has 0 records to process")
         return
 
     table = pyarrow.Table.from_pandas(df)