There are other databases today that do real time analytics (ClickHouse, Apache Druid, StarRocks along with Apache Pinot). I'd look at the ClickHouse Benchmark to see who are the competitors in that space and their relative performance.
It appears to make very different tradeoffs in a number of areas so that makes it a potential useful alternative. In particular transactional DML will make it much more convenient for workloads involving mutation. Plus as you suggested, having a proper Cost-Based Optimizer should make joins more efficient (I find ClickHouse joins to be fine for typical OLAP patterns but they do break down in creative queries...)
It's a bummer though that the deployment model is so complicated. One thing I truly like about ClickHouse is its ability to wring every drop of performance out of a single machine, with a super simple operational model. Being able to scale to a cluster is great but having to start there is Not Great.
Somehow StarRocks dudes appear in every relevant post with this false claim.
My experience with Clickhouse is that its joins are not performant enough to be useful. So the best practice in most cases is to denormalize. I should have been more specific in my earlier comment.
its common best practice on any database, because if both joined tables don't fit memory, then merge join is O(nlogn) operation which indeed many times slower than querying denormalized schema, which will have linear execution time.