'Towards Open Options Chains Part III: Getting Started with Airflow' optionstrading dataengineering
from airflow.models import Variable # Get variables API_KEY=Variable.get DB_PASSWORD=Variable.getto connect Python to our Postgres database. However, since we're using Airflow to run the ETL job, we can leverage Airflow's. Hooks simplify the code needed to interact with other services . See below for a simple comparison for our use case:
# The psycopg2 way import psycopg2 as pg2 from airflow.models import Variable DB_PASSWORD=Variable.get conn=pg2.connect query='...' with conn.cursor as cursor: cursor.execute df=pd.DataFrame, columns=...) conn.close # Using Airflow Hooks from airflow.providers.postgres.hooks.postgres import PostgresHook pg_hook=PostgresHook query='...' df=pg_hook.get_pandas_dfdatabase, we have to repeat the code above. First, using the Hook method achieves the same thing in fewer lines of code.
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