clari-deploy-integration

# Clari Deploy Integration ## Overview Deploy Clari export pipelines to production environments: Airflow DAGs, AWS Lambda, or Google Cloud Functions for scheduled, serverless execution. ## Instructions ### Airflow DAG ```python # dags/clari_export_dag.py from airflow import DAG from airflow.operators.python import PythonOperator from airflow.models import Variable from datetime import datetime, timedelta def export_clari_forecast(**context): from clari_client import ClariClient, ClariConfig client = ClariClient(ClariConfig( api_key=Variable.get("clari_api_key"), )) period = context["params"].get("period", "2026_Q1") data = client.export_and_download("company_forecast", period) entries = data.get("entries", []) context["ti"].xcom_push(key="entry_count", value=len(entries)) # Load to warehouse here dag = DAG( "clari_daily_export", schedule_interval="0 6 * * *", start_date=datetime(2026, 1, 1), catchup=False, default_args={"retries": 2, "retry_delay": timedelta(minutes=5)}, ) export_task = PythonOperator( task_id="export_forecast", python_callable=export_clari_forecast, dag=dag, ) ``` ### AWS Lambda ```python # lambda_handler.py import json import boto3 from clari_client import ClariClient, ClariConfig def handler(event, context): ssm = boto3.client("ssm") api_key = ssm.get_parameter( Name="/clari/api-key", WithDecryption=True )["Parameter"]["Value"] client = ClariClien...