Re: ad-hoc queries impacting production, you can mitigate this by pointing your data mesh to a read replica of the prod DB (which you can colocate with it to avoid bandwidth costs). Admittedly at that point you might be approaching a warehousing use case.
Re: maintaining the mapping. Sure that's a tradeoff. But I'd rather manage declarative FDW definitions than a programmatic ETL pipeline. Yes, handling schema changes is non-trivial (a naive starting point is periodic introspection of the remote table), but that's true in an ETL pipeline too. As you say, you're moving the cost from the ETL pipeline to the FDW definition. But what if we can lower the cost of the "FDW definition" (a.k.a. setting up and maintaining the data mesh)?
From our perspective, at least with the product we're building, we think we can make a data mesh that's low maintenance enough that it makes sense as a first default before ETL. But the two aren't mutually exclusive. A data mesh can connect multiple warehouses just as easily as it can your production databases. For example, you could move data (ETL) from your data sources to your Snowflake and BigQuery warehouses, and then connect them both to your data mesh.
Also, a mesh can add value as a unified access layer that proxies queries and applies data governance rules (e.g. sharing data, access control, column masking, query whitelisting, rewriting, rate limiting, auditing, firewalling, etc.). It would be hard to do this in your warehouse, because as soon as you have another one, or there's some data that you can't ETL into it, you lose any benefits of its access layer. (e.g. Maybe you have two warehouses because you haven't fully migrated to one, or for cost reasons like storing FireBase clickstream data in BigTable that you can't move to Snowflake with the rest of your data. Now you need to maintain two sets of accounts and ACLs for each warehouse.).
(And for what it's worth -- this is a bit off topic of the current thread -- we do recognize there is often a use case for warehousing when the tradeoffs of federation aren't worth it. We can actually act as a warehouse too, which makes it easy to start with a data mesh but have the option to bail out to warehousing for the specific data assets that need it. Ideally, you can just click a button that says something like "snapshot data" or "schedule query" or "replicate data source." To ingest and store the data, we can leverage the concept we introduced of "data images" for storing versioned snapshots of data in delta-compressed, content-addressable chunks of hot-swappable cstore_fdw files. You can create data images with a declarative, Dockerfile-like syntax called Splitfiles. They look something like FROM upstream:tag IMPORT { SELECT x, y FROM ... }). You get full provenance and can `sgr rebuild` an updated image only when its upstream changes, just like Docker.)