diff --git a/src/analytics/backend/service/SparkStreaming.py b/src/analytics/backend/service/SparkStreaming.py
index 26d3c26d878795eb5a07cbf228f2e15d02d4b500..73aa7502596fada37a3881da3f6774e95c70f257 100644
--- a/src/analytics/backend/service/SparkStreaming.py
+++ b/src/analytics/backend/service/SparkStreaming.py
@@ -44,7 +44,7 @@ def DefiningRequestSchema():
             StructField("kpi_value"  ,  DoubleType()  , True)
         ])
 
-def get_aggregations(oper_list):
+def GetAggregations(oper_list):
     # Define the possible aggregation functions
     agg_functions = {
         'avg'  :  round(avg    ("kpi_value"), 3) .alias("avg_value"),
@@ -56,7 +56,7 @@ def get_aggregations(oper_list):
     }
     return [agg_functions[op] for op in oper_list if op in agg_functions]   # Filter and return only the selected aggregations
 
-def apply_thresholds(aggregated_df, thresholds):
+def ApplyThresholds(aggregated_df, thresholds):
     # Apply thresholds (TH-Fail and TH-RAISE) based on the thresholds dictionary on the aggregated DataFrame.
     
     # Loop through each column name and its associated thresholds
@@ -114,9 +114,9 @@ def SparkStreamer(kpi_list, oper_list, window_size=None, win_slide_duration=None
                                            ),
                                     col("kpi_id")
                                 ) \
-                                .agg(*get_aggregations(oper_list))
+                                .agg(*GetAggregations(oper_list))
         # Apply thresholds to the aggregated data
-        thresholded_stream_data = apply_thresholds(windowed_stream_data, thresholds)
+        thresholded_stream_data = ApplyThresholds(windowed_stream_data, thresholds)
 
         # --- This will write output on console: FOR TESTING PURPOSES
         # Start the Spark streaming query