Thanks for writing in Jimmy,

We don't have this on our roadmap currently, but I believe you would be able to accomplish this workflow using a dbt transformation job that periodically extracts data and writes to Google Drive. 

dbt supports integrated python code, and the script you would need to write might look something like this (I did not write this myself, the below is output from an LLM — you would need to review and fix-up the code): 

import csv
import os
import google.auth
from googleapiclient.discovery import build
from googleapiclient.http import MediaFileUpload
import pyodbc  # or databricks-sql-connector

# 1) Connect to Databricks
connection = pyodbc.connect(
    'DSN=Databricks',  # or the appropriate connection string
    autocommit=True
)
cursor = connection.cursor()

# 2) Query your newly refreshed table
cursor.execute("SELECT * FROM your_schema.export_data")
rows = cursor.fetchall()
columns = [desc[0] for desc in cursor.description]

# 3) Write rows to a CSV
csv_filename = "export_data.csv"
with open(csv_filename, "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(columns)
    writer.writerows(rows)

# 4) Upload to Google Drive
# Make sure you’ve set up Google API credentials (service account JSON, etc.)
creds, _ = google.auth.default(scopes=["https://www.googleapis.com/auth/drive.file"])
drive_service = build('drive', 'v3', credentials=creds)

file_metadata = {
    'name': csv_filename,
    'parents': ['<your_drive_folder_id>']  # folder you want in Drive
}
media = MediaFileUpload(csv_filename, mimetype='text/csv')

uploaded_file = drive_service.files().create(
    body=file_metadata,
    media_body=media,
    fields='id'
).execute()

print("Uploaded CSV to Drive - File ID:", uploaded_file.get('id'))