feat(etl): orchestre l'import historique avec Airflow

docker-compose.yml

@@ -36,6 +36,7 @@ x-airflow-common: &airflow-common
   volumes:
     - ./etl/airflow/dags:/opt/airflow/dags
     - ./etl/airflow/plugins:/opt/airflow/plugins
+    - ./data/raw:/opt/data/raw:ro
     - airflow_logs:/opt/airflow/logs
     - airflow_ml_state:/opt/ml/state
   restart: unless-stopped

etl/airflow/dags/historical_import.py

@@ -0,0 +1,43 @@
+"""DAG d'import du dataset historique EnerVision (issue #119).
+
+Orchestre le pipeline existant `app.etl.historical_import` sans dupliquer sa logique ETL.
+Le dataset historique sert à initialiser l'environnement : le DAG reste donc manuel.
+
+Le backend est exécuté dans l'environnement `/opt/backend` embarqué dans l'image Airflow,
+sur le même patron que le DAG `alertes` (ADR 0008).
+"""
+
+from __future__ import annotations
+
+from datetime import datetime, timedelta
+
+from airflow.models.dag import DAG
+from airflow.operators.bash import BashOperator
+
+COMMANDE_BACKEND = "cd /opt/backend && env -u VIRTUAL_ENV uv run --no-sync python -m"
+
+CSV_PATH = "/opt/data/raw/all_sites_combined.csv"
+METADATA_PATH = "/opt/data/raw/dataset_metadata.json"
+
+with DAG(
+    dag_id="historical_import",
+    description="Importe le dataset historique CSV/JSON dans dataset, site et reading.",
+    schedule=None,
+    start_date=datetime(2026, 1, 1),
+    catchup=False,
+    max_active_runs=1,
+    tags=["etl", "historical"],
+) as dag:
+    BashOperator(
+        task_id="import_historical",
+        bash_command=(
+            f"{COMMANDE_BACKEND} app.etl.historical_import "
+            f"--csv {CSV_PATH} "
+            f"--metadata {METADATA_PATH} "
+            "--source-timezone UTC "
+            "--batch-size 1000"
+        ),
+        retries=1,
+        retry_delay=timedelta(minutes=2),
+        execution_timeout=timedelta(minutes=30),
+    )