This architecture is better for OLTP because all maintenance operations are moved to storage AND it has all other benefits such as LTAP that emerge from having a scalable storage.
Separately, I understand taking care of it at storage level, but still don’t get “unifying storage” or “zero copy”.
Anyways, I’ll stop now. Good to see all the innovation happening on converging OLTP/OLAP front. Each with a different approach and perspective. :)
Moonlink was arguably one of the best ZeroETL(mirroring) solution, and there's a reason we built LTAP instead (not just because it's cool and we can do it)
To me, two big issues with CDC/mirroring are: 1. For ad-hoc querying, CDC is usually not there when you need them, and it is too expensive to maintain for all tables if you don't query them often. 2. For true data pipeline, a simple mirror is not enough, you will end up building all kinds of transformation and it essentially becomes ETL like spark.
One small issue is CDC + merge into columnstore means huge write amplification if you want fresh read on Analytics side, it is absurd especially for lakehouse tables (I have seen 100X for many OLTP workloads).
The only solution I found that we can trust agents creating, managing, running OLTP & OLAP queries is LTAP.