Fivetran is an ELT/ETL tool that can sync data from a 160+ sources to your data warehouse. See link: https://www.fivetran.com/
If you are synching data to BigQuery, one of the first things you should do is to partition and cluster your tables. This helps keeping your cost down and improve performance. Tables synched by Fivetran is by default not partitioned or clustered.
This repository contains a script that recreates Fivetran tables as partitioned and clustered.
It partitions by _fivetran_synced, which is a timestamp added by Fivetran for when the data was loaded into your datawarehouse. It clusters by _fivetran_id and a column called id if available, else it uses columns called something with _id.
I have run this script inside of my GCP account (on a vm running Jupyter lab) where the authentication was done by the service account of the machine. If you are running outside of GCP you need to add some authentication. For info about that and getting started with the Python BigQuery library read here
project_id: Your GCP project id, where the datasets are.
fivetran_datasets: A list of your Fivetran dataset names in BigQuery