diff --git a/cloud-functions/python/functions/consolidation/main.py b/cloud-functions/python/functions/consolidation/main.py
index e77422f..32a9a01 100644
--- a/cloud-functions/python/functions/consolidation/main.py
+++ b/cloud-functions/python/functions/consolidation/main.py
@@ -66,35 +66,24 @@ def consolidation(request):
         test_data = test_doc.to_dict()
         data.append(test_data)
 
-    test_df = pd.DataFrame(data)
+    df = pd.DataFrame(data)
 
-    query = db.collection("patientData").where(filter=FieldFilter("createdAt", ">=", start_date)).where(filter=FieldFilter("createdAt", "<", end_date))
-    data = []
-    docs = query.stream()
-    for doc in docs:
-        row_data = doc.to_dict()
-        data.append(row_data)
-
-    user_df = pd.DataFrame(data)
-
-    df = pd.merge(test_df, user_df, on='_id')
-
-    output_filename = f'data_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'
+    output_filename = f'/tmp/data_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'
 
     output_path = os.getcwd() + path_delim + output_filename
     writer = pd.ExcelWriter(output_path, engine = 'openpyxl')
     df = df[(df['testTime'] > start_date) & (df['testTime'] <= end_date)]
     df = df.sort_values(by=['testTime'], ascending=False)
     
-    column_names = ["_id", "classificationResult", "deviceRatioClass",  "prdClassification", "slopeRatioClass", "predictedDenovixRatio", "slopeRatio", "calculatedRatio", "deviceRatio", "kitSerial", "led1Gain1", "led1Gain2", "led1Gain4", "abs1", "led1Average", "led1Buffer", "led1Sample", "led2Gain1", "led2Gain2", "led2Gain4", "abs2", "led2Average", "led2Buffer", "led2Sample", "led3Gain1", "led3Gain2", "led3Gain4", "hb3", "abs3", "led3Average", "led3Buffer", "led3Sample", "led4Gain1", "led4Gain2", "led4Gain4", "hb4", "abs4", "led4Average", "led4Average", "led4Buffer", "led4Sample", "batteryLevel", "batteryVoltage", "solution", "concentration", "volume", "errorMessages", "deviceId", "deviceSerialNumber", "appVersion", "name_y", "testTime"]
+    column_names = ["_id", "classificationResult", "deviceRatioClass",  "prdClassification", "slopeRatioClass", "predictedDenovixRatio", "slopeRatio", "calculatedRatio", "deviceRatio", "kitSerial", "led1Gain1", "led1Gain2", "led1Gain4", "abs1", "led1Average", "led1Buffer", "led1Sample", "led2Gain1", "led2Gain2", "led2Gain4", "abs2", "led2Average", "led2Buffer", "led2Sample", "led3Gain1", "led3Gain2", "led3Gain4", "hb3", "abs3", "led3Average", "led3Buffer", "led3Sample", "led4Gain1", "led4Gain2", "led4Gain4", "hb4", "abs4", "led4Average", "led4Average", "led4Buffer", "led4Sample", "batteryLevel", "batteryVoltage", "solution", "concentration", "volume", "errorMessages", "deviceId", "deviceSerialNumber", "appVersion", "name", "testTime"]
     common_columns = [col for col in column_names if col in df.columns]
     df = df[common_columns]
-
-    df.rename(columns={'deviceSerialNumber': "loginId", "name_y": "name"}, inplace = True)
-    # df = df.reindex(sorted(df.columns), axis=1)
     
-    df = df.drop_duplicates()
+    df.rename(columns={'deviceSerialNumber': "loginId"}, inplace = True)
+    # df = df.reindex(sorted(df.columns), axis=1)
 
+    df = df.drop_duplicates()
+    
     df.to_excel(writer, sheet_name = 'data', index=False)
     writer.close()