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With PR #5666, OnDemandFeatureView supports aggregations. But it is exclusive with transformation.
the reason is the infer_schema will break if having both defined in feature views.
This can be solved by reworking the infer_schema.

    def infer_features(self) -> None:
        random_input = self._construct_random_input(singleton=self.singleton)
        inferred_features = self.feature_transformation.infer_features(
            random_input=random_input, singleton=self.singleton
        )

Schema inference doesn't run the aggregation step - it only:

1. Looks at the source feature view's schema
2. Creates fake random data matching those columns
3. Passes it directly to the UDF
   But at runtime, the UDF receives aggregated columns, not the original source columns.
   So the UDF would need to be written for aggregated input:

  def my_odfv(inputs):
      return {"trips_doubled": inputs["sum_trips"] * 2}  # expects sum_trips

But schema inference passes it:

  {"trips": [10], "rating": [4.5]}

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