I would like to automate a csv file extraction process from Google BigQuery to a Google Cloud Storage Bucket, and from the latter to an external server with two Python scripts, could you help me please? I would appreciate it.
2 Answers
1
votes
For extracting from BigQuery in Python, you can use the Python Client for Google BigQuery.
The below snippet based on this repository should get you going:
# client = bigquery.Client()
# bucket_name = 'my-bucket'
project = "bigquery-public-data"
dataset_id = "samples"
table_id = "shakespeare"
destination_uri = "gs://{}/{}".format(bucket_name, "shakespeare.csv")
dataset_ref = bigquery.DatasetReference(project, dataset_id)
table_ref = dataset_ref.table(table_id)
extract_job = client.extract_table(
table_ref,
destination_uri,
# Location must match that of the source table.
location="US",
) # API request
extract_job.result() # Waits for job to complete.
print(
"Exported {}:{}.{} to {}".format(project, dataset_id, table_id, destination_uri)
)
In order to post the export to another server, you can use the Cloud Storage Client Library for Python to post the CSV file to your server or service of choice.
-1
votes
As per my knowledge, BigQuery can't export/download query result to GCS or Local File. You can keep it in a temporary / stagging table and then use code like below to export to gcs:
https://cloud.google.com/bigquery/docs/exporting-data#exporting_table_data
So you can put this in a container and deploy it as cloudrun service and call this from cloud scheduler.