There are design patterns / architectures that data engineers often employ to make this less "sucky". Data modeling is magical! (Specifically talking about things like datelist and cumulative tables)

you are doing data warehousing wrong, need to learn basics of data warehousing best practices.

Data Warehouse consists of Slowly Changing Dimensions and Facts. none of these require updates

If you can afford rare, batched updates, it sucks much less.

Anyway, yes, if your data is highly mutable, or you cannot do batch writes, then yes, Clickhouse is a wrong choice. Otherwise... it is _really_ hard to ignore 50x (or more) speedup.

Logs, events, metrics, rarely updated things like phone numbers or geocoding, archives, embeddings... Whoooop — it slurps entire Reddit in 48 seconds. Straight from S3. Magic.

If you still want really fast analytics, but have more complex scenarios and/or data loading practices, there's also Kinetica... if you can afford the price. For tiny datasets (a few terabytes), DuckDB might be a great choice too. But Postgres is usually a wrong thing to make work.