PS: we do have lots of load
In my experience all these features sound nice on paper. But you quickly run into practical issues that are far easier when you know approximate information about the state.
E.g. Developing a model? you might just want a subset/batch data. Doing BI/Analytics? are you going to continuously tax your server to recompute? The argument about recommender systems is also honestly flimsy, having built and applied such systems to live traffic at very large scale (more than hundreds of millions of users). There is only a small advantage from being able to quickly reconfigure flows. In most cases you have a single baseline model which you compare against for a small fraction of the traffic. The real complexity/gains in recommender systems lie in choice of algorithm/hyper-parameters/features, not on continuous multi armed bandits with 1000 different models applied simultaneously while waiting an infinite amount of time to produce any statistically meaningful answer. In fact for a website like this one, recommender systems can only provide so much advantage.
There are actually several really good specialized use cases, e.g. Google secmon-tools uses a system like this one.
[1] https://web.stanford.edu/class/cs259d/lectures/Session11.pdf
I really do not understand how such a strong set of conclusions can be drawn out of so little information.
By logging all the interaction events and computing application state via a stream processor, you get all the benefit's of a log-centric architecture. Building different services as "views" over the history of interactions, forking the stream to test new applications, replaying data to test different recommender models, ramping up models on historic data and falling in with real-time data for continuous updates. That all becomes the same thing.
And for many use cases, you get better performance on less hardware because you have a simpler concurrency model.