At the moment, we are focused on scaling up with a single instance. While there are a couple of strategies for scaling out, none of our current methods support scaling a single view beyond a single instance. This isn't really a technical limitation but rather more of a testing and supportability limitation. Timely and Differential (the incremental computation frameworks used by Materialize) suport scale-out and have been extensively tested in scale-out scenarios.

Do you have a limit in mind on how large you're willing to go? A brief bit of napkin math shows that, at 1KB per record, your largest view should fit within 100GB of memory. This is easily supported by Materialize and I can certainly understand if this exceeds your appetite!

If you're interested in reducing the size of the in-memory dataset, does your fact table have a temporal dimension to it? By default, Materialize stores data for all time (like a database) and you can write views in such a way that it will only materialize recent data (like a stream processor). Our co-founder Frank wrote a blog post[1] detailing how to do this.

I'm honestly not sure how TAIL-ing a non-materialized view would perform. Sounds like something that would be fun to test!

Happy to chat about this further in our community Slack channel if you have more questions.

[1] https://materialize.com/temporal-filters/