airflow-google-ads-api-report-fetcher 0.0.3

Library for running google-ads-api-report-fetcher in Apache Airflow.

Using gaarf in Airflow

If you want to use Apache Airflow to run any gaarf-based projects you can use airflow-google-ads-api-report-fetcher package.

Install it with pip install airflow-google-ads-api-report-fetcher - it will make airflow_gaarf library available.

Install the latest development version with pip install -e git+https://github.com/google/ads-api-report-fetcher.git#egg=airflow-google-ads-api-report-fetcher\&subdirectory=py/airflow_gaarf

The library comes with two operators - GaarfOperator and GaarfBqOperator which can be used to simplify executing google_ads_queries and bq_queries respectively.

Setup

Connections

Template pipeline expects two type of connections - go to Admin - Connections, add new connection (type Generic) and in Extra add the values specified below:

• google_ads_default

    {"google_ads_client":
    	{"developer_token": "",
    	"client_id": "",
    	"client_secret": "",
    	"refresh_token": "",
    	"login_customer_id": "",
    	"client_customer_id": "",
    	"use_proto_plus": "true"
    	}
    }
  

• gcp_conn_id

  {"cloud":
    	{"project_id": "your-project"}
  }
  

Dag

Once the above connections were setup you may proceed to configuring DAG.

src/template_dag.py contains a template DAG where you need to specify the following:

• solution_directory - absolute path to the directory containing queries.
• path_to_config - yaml file that is generated by running gaarf and gaarf-bq commands

Don't forget to replace template_gaarf_dag to DAG name that aligns with your naming conventions and update schedule_interval and start_date.