Run this DAG

1. Install Astronomer CLISkip if you already have the CLI

2. Initate the project:

3. Copy and paste the code below into a file in the

dags
directory.

4. Add the following to your requirements.txt file:

5. Run the DAG:

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""
Example Airflow DAG that shows how to use FacebookAdsReportToGcsOperator.
"""
import os
from facebook_business.adobjects.adsinsights import AdsInsights
from airflow import models
from airflow.providers.google.cloud.operators.bigquery import (
BigQueryCreateEmptyDatasetOperator,
BigQueryCreateEmptyTableOperator,
BigQueryDeleteDatasetOperator,
BigQueryExecuteQueryOperator,
)
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.transfers.facebook_ads_to_gcs import FacebookAdsReportToGcsOperator
from airflow.providers.google.cloud.transfers.gcs_to_bigquery import GCSToBigQueryOperator
from airflow.utils.dates import days_ago
# [START howto_GCS_env_variables]
GCP_PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "free-tier-1997")
GCS_BUCKET = os.environ.get("GCS_BUCKET", "airflow_bucket_fb")
GCS_OBJ_PATH = os.environ.get("GCS_OBJ_PATH", "Temp/this_is_my_report_csv.csv")
GCS_CONN_ID = os.environ.get("GCS_CONN_ID", "google_cloud_default")
DATASET_NAME = os.environ.get("DATASET_NAME", "airflow_test_dataset")
TABLE_NAME = os.environ.get("FB_TABLE_NAME", "airflow_test_datatable")
# [END howto_GCS_env_variables]
# [START howto_FB_ADS_variables]
FIELDS = [
AdsInsights.Field.campaign_name,
AdsInsights.Field.campaign_id,
AdsInsights.Field.ad_id,
AdsInsights.Field.clicks,
AdsInsights.Field.impressions,
]
PARAMS = {'level': 'ad', 'date_preset': 'yesterday'}
# [END howto_FB_ADS_variables]
with models.DAG(
"example_facebook_ads_to_gcs",
schedule_interval=None, # Override to match your needs
start_date=days_ago(1),
) as dag:
create_bucket = GCSCreateBucketOperator(
task_id="create_bucket",
bucket_name=GCS_BUCKET,
project_id=GCP_PROJECT_ID,
)
create_dataset = BigQueryCreateEmptyDatasetOperator(
task_id="create_dataset",
dataset_id=DATASET_NAME,
)
create_table = BigQueryCreateEmptyTableOperator(
task_id="create_table",
dataset_id=DATASET_NAME,
table_id=TABLE_NAME,
schema_fields=[
{'name': 'campaign_name', 'type': 'STRING', 'mode': 'NULLABLE'},
{'name': 'campaign_id', 'type': 'STRING', 'mode': 'NULLABLE'},
{'name': 'ad_id', 'type': 'STRING', 'mode': 'NULLABLE'},
{'name': 'clicks', 'type': 'STRING', 'mode': 'NULLABLE'},
{'name': 'impressions', 'type': 'STRING', 'mode': 'NULLABLE'},
],
)
# [START howto_operator_facebook_ads_to_gcs]
run_operator = FacebookAdsReportToGcsOperator(
task_id='run_fetch_data',
start_date=days_ago(2),
owner='airflow',
bucket_name=GCS_BUCKET,
params=PARAMS,
fields=FIELDS,
gcp_conn_id=GCS_CONN_ID,
object_name=GCS_OBJ_PATH,
)
# [END howto_operator_facebook_ads_to_gcs]
load_csv = GCSToBigQueryOperator(
task_id='gcs_to_bq_example',
bucket=GCS_BUCKET,
source_objects=[GCS_OBJ_PATH],
destination_project_dataset_table=f"{DATASET_NAME}.{TABLE_NAME}",
write_disposition='WRITE_TRUNCATE',
)
read_data_from_gcs_many_chunks = BigQueryExecuteQueryOperator(
task_id="read_data_from_gcs_many_chunks",
sql=f"SELECT COUNT(*) FROM `{GCP_PROJECT_ID}.{DATASET_NAME}.{TABLE_NAME}`",
use_legacy_sql=False,
)
delete_bucket = GCSDeleteBucketOperator(
task_id="delete_bucket",
bucket_name=GCS_BUCKET,
)
delete_dataset = BigQueryDeleteDatasetOperator(
task_id="delete_dataset",
project_id=GCP_PROJECT_ID,
dataset_id=DATASET_NAME,
delete_contents=True,
)
create_bucket >> create_dataset >> create_table >> run_operator >> load_csv
load_csv >> read_data_from_gcs_many_chunks >> delete_bucket >> delete_dataset