diff --git a/sdk/python/feast/client.py b/sdk/python/feast/client.py
index f649320749..0254f4187b 100644
--- a/sdk/python/feast/client.py
+++ b/sdk/python/feast/client.py
@@ -17,6 +17,7 @@
 import os
 import time
 from collections import OrderedDict
+from math import ceil
 from typing import Dict, Union
 from typing import List
 from urllib.parse import urlparse
@@ -36,7 +37,6 @@
     GetFeatureSetResponse,
 )
 from feast.core.CoreService_pb2_grpc import CoreServiceStub
-from feast.core.FeatureSet_pb2 import FeatureSetStatus
 from feast.feature_set import FeatureSet, Entity
 from feast.job import Job
 from feast.loaders.abstract_producer import get_producer
@@ -336,11 +336,15 @@ def get_batch_features(
                 "feature_set_name:version:feature_name".
 
             entity_rows (Union[pd.DataFrame, str]):
+                Either:
                 Pandas dataframe containing entities and a 'datetime' column.
                 Each entity in a feature set must be present as a column in this
                 dataframe. The datetime column must contain timestamps in
                 datetime64 format.
 
+                Or:
+                A file path in AVRO format representing the entity rows.
+
         Returns:
             feast.job.Job:
                 Returns a job object that can be used to monitor retrieval
@@ -794,7 +798,7 @@ def _read_table_from_source(
 
     # Write table as parquet file with a specified row_group_size
     tmp_table_name = f"{int(time.time())}.parquet"
-    row_group_size = min(int(table.num_rows/max_workers), chunk_size)
+    row_group_size = min(ceil(table.num_rows / max_workers), chunk_size)
     pq.write_table(table=table, where=tmp_table_name,
                    row_group_size=row_group_size)
 
diff --git a/sdk/python/feast/feature_set.py b/sdk/python/feast/feature_set.py
index 28381891f5..42979ea911 100644
--- a/sdk/python/feast/feature_set.py
+++ b/sdk/python/feast/feature_set.py
@@ -19,8 +19,6 @@
 
 import pandas as pd
 import pyarrow as pa
-from feast.core.FeatureSet_pb2 import FeatureSet as FeatureSetProto
-from feast.core.FeatureSet_pb2 import FeatureSetMeta as FeatureSetMetaProto
 from feast.core.FeatureSet_pb2 import FeatureSetSpec as FeatureSetSpecProto
 from feast.entity import Entity
 from feast.feature import Feature, Field
diff --git a/sdk/python/setup.py b/sdk/python/setup.py
index 9ac7225e80..420d1a39af 100644
--- a/sdk/python/setup.py
+++ b/sdk/python/setup.py
@@ -32,7 +32,7 @@
     "googleapis-common-protos==1.*",
     "google-cloud-bigquery-storage==0.7.*",
     "grpcio==1.*",
-    "pandas==0.*",
+    "pandas>=0.25.0",
     "pandavro==1.5.*",
     "protobuf>=3.10",
     "PyYAML==5.1.*",
diff --git a/sdk/python/tests/test_client.py b/sdk/python/tests/test_client.py
index f979c5a55d..9ef6e3d56f 100644
--- a/sdk/python/tests/test_client.py
+++ b/sdk/python/tests/test_client.py
@@ -385,36 +385,6 @@ def test_feature_set_ingest_success(self, dataframe, client, mocker):
             # Ingest data into Feast
             client.ingest("driver-feature-set", dataframe)
 
-    @pytest.mark.parametrize("dataframe,exception", [(dataframes.GOOD, TimeoutError)])
-    def test_feature_set_ingest_fail_if_pending(
-        self, dataframe, exception, client, mocker
-    ):
-        with pytest.raises(exception):
-            driver_fs = FeatureSet(
-                "driver-feature-set",
-                source=KafkaSource(brokers="kafka:9092", topic="test"),
-            )
-            driver_fs.add(Feature(name="feature_1", dtype=ValueType.FLOAT))
-            driver_fs.add(Feature(name="feature_2", dtype=ValueType.STRING))
-            driver_fs.add(Feature(name="feature_3", dtype=ValueType.INT64))
-            driver_fs.add(Entity(name="entity_id", dtype=ValueType.INT64))
-
-            # Register with Feast core
-            client.apply(driver_fs)
-            driver_fs = driver_fs.to_proto()
-            driver_fs.meta.status = FeatureSetStatus.STATUS_PENDING
-
-            mocker.patch.object(
-                client._core_service_stub,
-                "GetFeatureSet",
-                return_value=GetFeatureSetResponse(feature_set=driver_fs),
-            )
-
-            # Need to create a mock producer
-            with patch("feast.client.get_producer") as mocked_queue:
-                # Ingest data into Feast
-                client.ingest("driver-feature-set", dataframe, timeout=1)
-
     @pytest.mark.parametrize(
         "dataframe,exception",
         [