Along withh several other issues I'm encountering, I am finding pandas dataframe to_sql being very slow

I am writing to an Azure SQL database and performance is woeful. This is a test database and it has S3 100DTU and one user, me as it's configuration. It's quick enough for most other tasks, but terrible for this.

I don't see any isssues in the database monitoring to suggest I've exceeded DTU or something.

This is my connection configuration, with appropriate redactions

connection_string = "Driver={ODBC Driver 18 for SQL Server};" \
                    "Server=azureservername.privatelink.database.windows.net;" \
                    "Database=datawarehousename;TrustServerCertificate=yes;" \
                    "Encrypt=yes;UID=dbuser;PWD=<pwd>"
connection_url = URL.create("mssql+pyodbc", query={"odbc_connect": connection_string})
engine = create_engine(connection_url, fast_executemany=True)

This is my dataframe.to_sql write to database

dfoutDB.to_sql("ResourceLogRpt", schema="reporting", con=engine, if_exists='append', chunksize=10000, index=False)

Changing chunksize isn't making a difference

In between reading data I've manipulated URL data to extract directory elements and parameters

Is there a way to verify the fast_executemany=True is doing anything?

Any other thoughts on diagnosing what is causing this to be slow.?

My cluster is 56 Gb and 8 cores DBR 1.4 with Scala 3.2.1, I would have thought enough as all other data frame manipulation is very fast, but if there's an impact that I need to understand I'd appreciate telling me what I misunderstood

Thanks

Peter