Dealing with issues in "test" predictons for single "items" (null values, standardization in place, etc)
You need to save the instructions for performing these preprocessing steps, not necessarily the dataset that you extracted them from.

See
Obtaining consistent one-hot encoding of train / production data
Binary Classification - One Hot Encoding preventing me using Test Set
one-hot-encoding categorical data gives error

In particular, sklearn preprocessors can be pickled, then used with their transform method in production, if you can use sklearn in deployment.  PMML also cam translate most transformers.  Or you can write your own simple transformer.

As to using newer data to rework the transformers, that's getting closer to retraining; in most settings, I would keep it in the same place as refitting the model: either both offline or both online.