Failure to connect

[alunap]

I have Azure databricks running in two regions (US and UK). I have been using the databricks-sql-connector successfully to connect to Hive Metastore tables in each for some months. Currently the clusters are not running all the time, but if I use the databricks connector it first spins up the appropriate cluster. I would love to be able to use SQLAlchemy instead, however.
I tried your test setup in a Jupyter notebook, but get a Operational Error.

import numpy as np
import pandas as pd
import os
from sqlalchemy import *
from sqlalchemy.engine import create_engine

def test_dialect(host, http_path, token):
engine = create_engine(
f"databricks+connector://token:{token}@{host}:443/default",
connect_args={"http_path": f"{http_path}"},
)
tables = inspect(engine).get_table_names()
print(tables)

region = "UK"
server_hostname= os.getenv(f'DATABRICKS_{region}HOSTNAME'),
http_path= os.getenv(f'DATABRICKS{region}HTTP'),
access_token = os.getenv(f'DATABRICKS{region}_TOKEN')
test_dialect(server_hostname, http_path, access_token)

gives:
OperationalError: (databricks.sql.exc.RequestError) Error during request to server
(Background on this error at: https://sqlalche.me/e/14/e3q8)

I do notice that this relies on PyHive, which is marked as unsupported.