You can do real-time feature engineering in your application as well. But we recommend to do it in a feature pipeline that also syncs the features to the feature store. We have a talk about it coming out at the spark/ai summit:

https://databricks.com/session_eu20/real-time-feature-engine...

Yeah in this type of architecture there is some sort of join required. Interestingly, @maycotte talking about the Molecula feature store – it serves real-time and historical features in the same storage layer. Significant performance gains with that approach.

If you want to reuse features, and not write a new pipeline for every model, you need JOINs. Both for the online and offline feature stores.