Druid can easily be extended through available 3rd party extensions and you can write your own to implement custom serialisation formats, aggregations, connect to new streaming systems, read directly from whatever cold storage you have etc.
In the Clickhouse model you have to work out a lot more of that stuff yourself though these days it can read from Kafka directly which is useful.
Some things that are important for Clickhouse vs Druid at big scale is the rather large difference in indexing approaches. Clickhouse uses bloom filters and other probabilistic data structures to index large chunks of data, for the most part though actually checking for rows requires a full scan of that chunk to strip false positives.
This is different to Druid which uses full inverted indices for dimension filtering.
The tradeoff is basically Clickhouse is cheaper, especially when scaling out but Druid is faster especially when the cluster is under heavy concurrent query load, like serving analytics dashboards or data exploration interfaces to users.
Clickhouse excels when you want to scan most but not all the data most of the time. Namely reporting or bulk analytics queries that will hit most rows in a block.
I consider both to be excellent databases.