You can use the below python function to create big query data transfer client and
copy datsets from one project to another by specifying source and target project id's. You can also schedule the data transfer.
In the method below it is set to 24 hours(daily).
def copy_dataset(override_values={}):
# [START bigquerydatatransfer_copy_dataset]
from google.cloud import bigquery_datatransfer
transfer_client = bigquery_datatransfer.DataTransferServiceClient()
destination_project_id = "my-destination-project"
destination_dataset_id = "my_destination_dataset"
source_project_id = "my-source-project"
source_dataset_id = "my_source_dataset"
# [END bigquerydatatransfer_copy_dataset]
# To facilitate testing, we replace values with alternatives
# provided by the testing harness.
destination_project_id = override_values.get(
"destination_project_id", destination_project_id
)
destination_dataset_id = override_values.get(
"destination_dataset_id", destination_dataset_id
)
source_project_id = override_values.get("source_project_id", source_project_id)
source_dataset_id = override_values.get("source_dataset_id", source_dataset_id)
# [START bigquerydatatransfer_copy_dataset]
transfer_config = bigquery_datatransfer.TransferConfig(
destination_dataset_id=destination_dataset_id,
display_name="Your Dataset Copy Name",
data_source_id="cross_region_copy",
params={
"source_project_id": source_project_id,
"source_dataset_id": source_dataset_id,
},
schedule="every 24 hours",
)
transfer_config = transfer_client.create_transfer_config(
parent=transfer_client.common_project_path(destination_project_id),
transfer_config=transfer_config,
)
print(f"Created transfer config: {transfer_config.name}")
# [END bigquerydatatransfer_copy_dataset]
return transfer_config