| I think it's more a matter of comparing minivans (cloud "DWH" engines) to sports cars (Clickhouse et al) here. Snowflake's performance characteristics & ops paradigm have always been more consistent with managed Spark than anything else. Thus the competition with Databricks. They have only recently started pretending to be anything than a low-maintenance batch processor with a nice managed storage abstraction, and their pricing model reinforces this. That being said, for now it's pretty hard currently to find something that gives you:
- Bottomless storage
- Always "OK" performance
- Complete consistency without surprises (synchronous updates, cross table transactions, snapshot isolation)
- The ability to happily chew through any size join and always return results
- Complete workload isolation ...all in one place, so people will probably be buying Snowflake credits for a few years yet. I'm excited about the coming generation--c.f. StarRocks and the Clickhouse roadmap--but the workloads and query patterns for OLAP and DWH only overlap due to marketing and the "I have a hammer" effect. I don't think the slight misuse of either type of engine is bad at small-to-medium scale, either. It's healthy to make "get it done" stacks with fewer query engines, fewer integration points, and already-known system limitations. |