diff --git a/backend_py/primary/primary/services/summary_delta_vectors.py b/backend_py/primary/primary/services/summary_delta_vectors.py
index 470cb61e7..572190758 100644
--- a/backend_py/primary/primary/services/summary_delta_vectors.py
+++ b/backend_py/primary/primary/services/summary_delta_vectors.py
@@ -4,7 +4,7 @@
 import pyarrow.compute as pc
 import numpy as np
 
-from .utils.summary_vector_table_helpers import validate_summary_vector_table
+from .utils.summary_vector_table_helpers import validate_summary_vector_table_pa
 
 
 @dataclass
@@ -29,8 +29,8 @@ def create_delta_vector_table(
 
     `Note`: Pre-processing of DATE-columns, e.g. resampling, should be done before calling this function.
     """
-    validate_summary_vector_table(first_vector_table, vector_name)
-    validate_summary_vector_table(second_vector_table, vector_name)
+    validate_summary_vector_table_pa(first_vector_table, vector_name)
+    validate_summary_vector_table_pa(second_vector_table, vector_name)
 
     joined_vector_table = first_vector_table.join(
         second_vector_table, keys=["DATE", "REAL"], join_type="inner", right_suffix="_second"
@@ -56,7 +56,7 @@ def create_realization_delta_vector_list(
     """
     Create a list of RealizationDeltaVector from the delta vector table.
     """
-    validate_summary_vector_table(delta_vector_table, vector_name)
+    validate_summary_vector_table_pa(delta_vector_table, vector_name)
 
     real_arr_np = delta_vector_table.column("REAL").to_numpy()
     unique_reals, first_occurrence_idx, real_counts = np.unique(real_arr_np, return_index=True, return_counts=True)
diff --git a/backend_py/primary/primary/services/sumo_access/summary_access.py b/backend_py/primary/primary/services/sumo_access/summary_access.py
index f636a8c3d..de5052ac7 100644
--- a/backend_py/primary/primary/services/sumo_access/summary_access.py
+++ b/backend_py/primary/primary/services/sumo_access/summary_access.py
@@ -16,7 +16,7 @@
     sort_table_on_real_then_date,
     is_date_column_monotonically_increasing,
 )
-from primary.services.utils.summary_vector_table_helpers import validate_summary_vector_table
+from primary.services.utils.summary_vector_table_helpers import validate_summary_vector_table_pa
 from primary.services.service_exceptions import (
     Service,
     NoDataError,
@@ -164,7 +164,7 @@ async def get_vector_async(
         table, vector_metadata = await self.get_vector_table_async(vector_name, resampling_frequency, realizations)
 
         # Verify that columns are as we expect
-        validate_summary_vector_table(table, vector_name, Service.SUMO)
+        validate_summary_vector_table_pa(table, vector_name, Service.SUMO)
 
         real_arr_np = table.column("REAL").to_numpy()
         unique_reals, first_occurrence_idx, real_counts = np.unique(real_arr_np, return_index=True, return_counts=True)
diff --git a/backend_py/primary/primary/services/utils/summary_vector_table_helpers.py b/backend_py/primary/primary/services/utils/summary_vector_table_helpers.py
index b866713dd..1a1bb9554 100644
--- a/backend_py/primary/primary/services/utils/summary_vector_table_helpers.py
+++ b/backend_py/primary/primary/services/utils/summary_vector_table_helpers.py
@@ -3,15 +3,18 @@
 from primary.services.service_exceptions import InvalidDataError, Service
 
 
-def validate_summary_vector_table(vector_table: pa.Table, vector_name: str, service: Service = Service.GENERAL) -> None:
+def validate_summary_vector_table_pa(
+    vector_table: pa.Table, vector_name: str, service: Service = Service.GENERAL
+) -> None:
     """
-    Check if the vector table is valid - single vector table should contain columns DATE, REAL, vector_name.
+    Check if the pyarrow vector table is valid.
 
-    Expect the pyarrow table to contain the following columns: DATE, REAL, vector_name.
+    Expect the pyarrow single vector table to contain the following columns: DATE, REAL, vector_name.
 
     Raises InvalidDataError if the table does not contain the expected columns.
     """
     expected_columns = {"DATE", "REAL", vector_name}
-    if set(vector_table.column_names) != expected_columns:
-        unexpected_columns = set(vector_table.column_names) - expected_columns
+    actual_columns = set(vector_table.column_names)
+    if actual_columns != expected_columns:
+        unexpected_columns = actual_columns - expected_columns
         raise InvalidDataError(f"Unexpected columns in table {unexpected_columns}", service)