There is a similarity between feature engineering pipelines that feed the feature store and the lambda architecture, in that you have two sinks for your data - one for batch applications (and creating train/test data) and one for real-time serving of features. However, there is typically only one feature engineering pipeline (to make sure features are consistent between training and serving), whereas lambda has two (one batch, one streaming).So, you could come back and say it is more like the kappa architecture, but it could be either a batch or streaming application computing the features and saving them to the feature store.