@@ -1,7 +1,8 @@
+import copy
 import functools
 import warnings
 from datetime import datetime, timedelta
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast
 
 import dill
 
@@ -161,6 +162,39 @@ def __init__(
             version=version,
         )
 
+    def __copy__(self) -> "BatchFeatureView":
+        fv = BatchFeatureView(
+            name=self.name,
+            mode=cast(Union[TransformationMode, str], self.mode),
+            source=cast(List["BatchFeatureView"], self.source_views)
+            if self.source_views
+            else self.batch_source,
+            sink_source=self.batch_source if self.source_views else None,
+            schema=self.schema,
+            ttl=self.ttl,
+            tags=self.tags,
+            online=self.online,
+            offline=self.offline,
+            description=self.description,
+            owner=self.owner,
+            org=self.org,
+            udf=self.udf,
+            udf_string=self.udf_string,
+            feature_transformation=self.feature_transformation,
+            batch_engine=self.batch_engine,
+            aggregations=self.aggregations,
+            enable_validation=self.enable_validation,
+            version=self.version,
+        )
+        fv.enabled = self.enabled
+        fv.state = self.state
+        fv.entities = self.entities
+        fv.features = copy.copy(self.features)
+        fv.entity_columns = copy.copy(self.entity_columns)
+        fv.projection = copy.copy(self.projection)
+        fv._raw_feature_transformation_proto = self._raw_feature_transformation_proto
+        return fv
+
     def get_feature_transformation(self) -> Optional[Transformation]:
         if not self.udf:
             return None
 
@@ -1,9 +1,11 @@
+import copy
 from datetime import timedelta
 
 import pandas as pd
 import pytest
 from typeguard import TypeCheckError
 
+from feast.aggregation import Aggregation
 from feast.batch_feature_view import BatchFeatureView
 from feast.data_format import AvroFormat
 from feast.data_source import KafkaSource
@@ -404,6 +406,46 @@ def transform_udf(df: pd.DataFrame) -> pd.DataFrame:
     assert second_deserialized.feature_transformation is not None
 
 
+def test_batch_feature_view_copy() -> None:
+    def transform_udf(df: pd.DataFrame) -> pd.DataFrame:
+        return df
+
+    bfv = BatchFeatureView(
+        name="test_batch_feature_view",
+        entities=[Entity(name="test_entity", join_keys=["entity_id"])],
+        schema=[
+            Field(name="entity_id", dtype=String),
+            Field(name="input_feature", dtype=Int64),
+        ],
+        source=FileSource(
+            name="test-source",
+            path="test_data.parquet",
+            timestamp_field="event_timestamp",
+        ),
+        udf=transform_udf,
+        mode="pandas",
+        batch_engine={"spark.sql.shuffle.partitions": "4"},
+        aggregations=[
+            Aggregation(
+                column="input_feature", function="sum", time_window=timedelta(days=1)
+            )
+        ],
+    )
+
+    bfv_copy = copy.copy(bfv)
+    assert isinstance(bfv_copy, BatchFeatureView)
+    assert bfv_copy == bfv
+    assert bfv_copy.mode == "pandas"
+    assert bfv_copy.udf is transform_udf
+    assert bfv_copy.feature_transformation is bfv.feature_transformation
+    assert bfv_copy.batch_engine == {"spark.sql.shuffle.partitions": "4"}
+    assert bfv_copy.aggregations == bfv.aggregations
+
+    projected = bfv[["input_feature"]]
+    assert isinstance(projected, BatchFeatureView)
+    assert projected.to_proto().spec.HasField("feature_transformation")
+
+
 def test_transformation_mode_serialization():
     """
     Test that transformation mode is properly serialized to proto and deserialized back.