This module contains Google Ad to GCS operators.

Module Contents



Saves list of customers on GCS in form of a csv file.

class*, bucket, object_name, gcp_conn_id='google_cloud_default', google_ads_conn_id='google_ads_default', gzip=False, impersonation_chain=None, api_version=None, **kwargs)[source]

Bases: airflow.models.BaseOperator

Saves list of customers on GCS in form of a csv file.

The resulting list of customers is based on your OAuth credentials. The request returns a list of all accounts that you are able to act upon directly given your current credentials. This will not necessarily include all accounts within the account hierarchy; rather, it will only include accounts where your authenticated user has been added with admin or other rights in the account.

See also

For more information on how to use this operator, take a look at the guide: Upload Google Ads Accounts to GCS

  • bucket (str) – The GCS bucket to upload to

  • object_name (str) – GCS path to save the csv file. Must be the full file path (ex. path/to/file.csv)

  • gcp_conn_id (str) – Airflow Google Cloud connection ID

  • google_ads_conn_id (str) – Airflow Google Ads connection ID

  • gzip (bool) – Option to compress local file or file data for upload

  • impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).

  • api_version (str | None) – Optional Google Ads API version to use.

template_fields: Sequence[str] = ('bucket', 'object_name', 'impersonation_chain')[source]

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

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