In the next version, SAP Data Services 4.2 SP6, you can use the application datastore to both extract data from and load data to your Google tables.
From SAP Data Services 4.2 SP7, Data Services has added support for Google Cloud Storage, so that you can upload and download files to Google Cloud Storage or local storage and load objects in Google Cloud Storage into Google BigQuery.
Moving on to version, SAP Data Services 4.2 SP8, The load_from_gcs_to_gbq function has been added to help you transfer data from a Google Cloud Storage into Google BigQuery tables. Also in SP08, you can create a Google BigQuery template table as a target in a data flow. When you execute the data flow, Data Services automatically creates the table in your Google account in the specified project and dataset.
For performance optimization, you could now use the new Google built-in function named gbq2file to optimize your software performance when you extract large volumes of data from Google BigQuery results to your local machine. This function exports results of a Google BigQuery to files in your Google Cloud Storage (GCS) and then transfers the data from GCS to a user-specified file on your local machine.
SAP Data Services 4.2 SP9 adds more performance optimization for Google BigQuery large data extraction and in the latest version as of writing, SAP Data Services 4.2 SP10, Data Services has added support for Date, Time, and Date time data types for Google BigQuery.
This evolution puts us at a point where using Data Services in conjunction with Google BigQuery is a really attractive proposition for large data workloads that require integration with on-premise corporate data workloads.