diff --git a/dags/fetch_maximo_daily.py b/dags/fetch_maximo_daily.py
index fd6106e..c41376f 100644
--- a/dags/fetch_maximo_daily.py
+++ b/dags/fetch_maximo_daily.py
@@ -71,19 +71,21 @@ def fetch_daily_maximo_data(**context):
     # Create a response callback function
     def response_callback(future):
         try:
-            # Get the response from the future, with a short timeout just to confirm 
+            # Get the response from the future, with a short timeout just to confirm
             # the request was properly initiated
             response = future.result(timeout=10)
             logger.info(
                 f"Request initiated successfully (Request ID: {request_id}), status: {response.status_code}"
             )
-            
+
             # We don't wait for the full response processing, as it may take longer than Airflow's task timeout
-            
+
         except requests.exceptions.Timeout:
             logger.error(f"Request connection timed out (Request ID: {request_id})")
         except Exception as e:
-            logger.error(f"Error initiating request (Request ID: {request_id}): {str(e)}")
+            logger.error(
+                f"Error initiating request (Request ID: {request_id}): {str(e)}"
+            )
 
     # Using ThreadPoolExecutor for async operation
     with ThreadPoolExecutor(max_workers=1) as executor:
@@ -94,13 +96,13 @@ def fetch_daily_maximo_data(**context):
             headers=headers,
             timeout=600,  # Increased timeout to 10 minutes for the actual API call
         )
-        
+
         # Add callback that will execute when future completes
         future.add_done_callback(response_callback)
-        
+
         # Don't wait for future to complete, let it run in background
         logger.info(f"Async request has been dispatched (Request ID: {request_id})")
-    
+
     # Push the request details to XCom for tracking
     result_dict = {
         "request_id": request_id,
@@ -108,10 +110,10 @@ def fetch_daily_maximo_data(**context):
         "timestamp": datetime.now().isoformat(),
         "message": "Fetch Daily Maximo request initiated asynchronously",
     }
-    
+
     ti = context["ti"]
     ti.xcom_push(key="fetch_result", value=result_dict)
-    
+
     return result_dict
 
 
@@ -126,8 +128,8 @@ def process_response(**context):
     if result:
         logger.info(f"Processing async request result: {result}")
         # Since we're using fire-and-forget pattern, we just acknowledge the request was made
-        
-        # In production, you might want to implement a separate DAG or task 
+
+        # In production, you might want to implement a separate DAG or task
         # to check the status of the asynchronous job later
 
         return True
@@ -146,7 +148,7 @@ default_args = {
 
 # Define the DAG
 dag = DAG(
-    "fetch_daily_maximo_data_async",
+    "fetch_daily_maximo_data",
     default_args=default_args,
     description="A DAG to fetch data from Maximo API endpoint asynchronously on a daily schedule",
     # Schedule to run daily at 21:00, 22:00, and 23:00
@@ -188,4 +190,4 @@ process_task = PythonOperator(
 
 # Set task dependencies
 check_running >> [skip_execution, fetch_task]
-fetch_task >> process_task
\ No newline at end of file
+fetch_task >> process_task