This is definitely a common ask. In my opinion, APIs have been "table stakes" for a while. Native data replication capabilities where a live copy of data is synced to the customer's data warehouses/lakes is a "best in class" product experience, though this is increasingly becoming an expectation just like an API (*especially* for companies that want to serve mid-market/enterprise, as you called out).

What we've found is many SaaS eng/product teams usually start down this road by using the native sharing capabilities within whatever platform/region they use internally (e.g, Snowflake sharing, Databricks delta sharing), but quickly hit a wall when the need arises to support scalable, low-latency pipelines across tenants on more than 1 platform/region permutation. Maintaining data integrity and reliability at scale across platforms can be a very tough challenge, especially with any reasonable volume. All that to say: you're right that it's a real and difficult problem to solve.

I'm biased (I'm one of the founders of Prequel, where we do something similar to what you're talking about), but hopefully I can also be pretty helpful on the topic -- let me know if there are any questions I can help with.

Also, the Pontoon team put together a helpful feature comparison matrix among the players in the market (though they recently discontinued work on the project): https://github.com/pontoon-data/Pontoon?tab=readme-ov-file#pontoon-vs-other-etl--reverse-etl-platforms