I think you're speaking here about 1. queue-based event-store systems, e.g. combining a compacting Kafka topic with a CQRS reducer agent to serve as a store-of-

I think you're speaking here about 1. queue-based event-store systems, e.g. combining a compacting Kafka topic with a CQRS reducer agent to serve as a store-of-record + snapshot-state representation respectively; 2. where you've likely restructured what were fundamentally CRUD-esque operations into CQRS commands and events, to hold in the store, just so that they can be folded back down to CRUD updates to a snapshot living in an RDBMS by the reducer? I do agree that this kind of system can get really messy/painful once you have a lot of data. But I'm thinking more about: 3. "the Dynamo/Hadoop model, in the small": a single-process client-server row-store, but where a table is made of immutable, read-only 64MB chunks, with the newest chunk being an "open" buffer that becomes "closed" as soon as it's filled up; and where these chunks are of a known file-format such that you could directly compose them outside the DBMS in parallel and push them to the DBMS as chunks to be ingested "whole" (i.e. "mounted" as micro-partitions of the table); 4. where the business-domain's types are already fundamentally immutable datatypes, that don't need any "projecting" into a CQRS representation; i.e. where the thing the DB exists to store (and query on!) is the immutable business data, not some latest-computed-state reduction over it, so there's no need to ever play a log into a CQRS reducer, let alone replay that log. I know that, for example, Clickhouse's MergeTree engine is at its core akin to the kind of system I'm describing in 3 above — but because Clickhouse is designed as a general-purpose RDBMS (and so still offers an API that includes UPDATE and DELETE) rather than being purpose-built for use-case 4 above, it needs to do a whole bunch of stuff on top of that constrained chunked-immutable storage primitive, to virtualize those updates/deletes pre-merge, and to present MVCC transactions. Same with, for another example, CouchDB: an immutable-data-store core, with a ton of logic on top to allow the user to pretend they can update/delete. If you imagine a version of Clickhouse or CouchDB that was solely focused on delivering use-case 4 above, then you could strip away all the "extra stuff." For use-case 4, the "64MB immutable-once-full micro-partitions" paradigm is literally all that's needed to losslessly convey all domain state; and so a storage engine akin to the one described in 3 is all you need to support it. (If you're wondering, the business domain I'm working in, where this pertains, is: analytical querying of blockchain data. All the "CQRS events" [blockchain blocks and their transactions] come from third parties, and are all guaranteed-immutable upon insert, if not guaranteed-canonical. [But canonicity can be tracked as a log of chain tip changes, like a git commit log.] If you don't care about blockchains, though, domains with similar needs for immutable append-only analytical stores include financial forensic accounting, and structured API audit-logging as state for rule engines inside Intrusion Detection Systems.)