create analytical and agg

dags/etl_food_security.py

@@ -1,6 +1,6 @@
 from airflow import DAG
 from airflow.operators.python import PythonOperator
-from datetime import datetime
+from datetime import datetime, timedelta
 
 # Import fungsi dari folder scripts
 from scripts.bigquery_raw_layer import (
@@ -19,11 +19,21 @@ from scripts.bigquery_dimesional_model import (
     run_dimensional_model,
 )
 
+from scripts.bigquery_analytical_layer import (
+    run_analytical_layer,
+)
+
+from scripts.bigquery_aggregate_layer import (
+    run_aggregation,
+)
+
 
 with DAG(
     dag_id            = "etl_food_security_bigquery",
+    description       = "Kimball ETL: FAO, World Bank, UNICEF to BigQuery (Bronze to Silver to Gold)",
     start_date        = datetime(2026, 3, 1),
     schedule_interval = "@daily",
+    schedule_interval = timedelta(days=3),
     catchup           = False,
     tags              = ["food-security", "bigquery", "kimball"]
 ) as dag:
@@ -63,5 +73,15 @@ with DAG(
         python_callable = run_dimensional_model
     )
     
+    task_analytical = PythonOperator(
+        task_id         = "analytical_layer_to_gold",
+        python_callable = run_analytical_layer
+    )
+    
+    task_aggregation = PythonOperator(
+        task_id         = "aggregation_to_gold",
+        python_callable = run_aggregation
+    )
+    
 
-    task_verify >> task_fao >> task_worldbank >> task_unicef >> task_staging >> task_cleaned >> task_dimensional
+    task_verify >> task_fao >> task_worldbank >> task_unicef >> task_staging >> task_cleaned >> task_dimensional >> task_analytical >> task_aggregation