In real life most people prefer taking a full snapshot each day because they don't have good solutions to these problems in batch systems (CDC is another story).
The whole point of ETL is to bring data from one database to another. The comparison of source and destination primary keys can be done in python outside of db. And should be done on entire partitions instead of individual rows. Eg. you only consider which 'day' partitions have been loaded, not which rows have been loaded.
"Negotiating with data provider" is never going to happen - SAP or IBM or whatever vendor of whatever you're integrating is not going to change how their systems work to make your life easier - more likely they would use it as an opportunity to pitch their reporting solution instead.
Meaning from simple data movement, you get need for CDC on source end, then the simple incremental movement, then deduplication on target end - and that one is pretty computationally expensive.
For small data and low refresh frequencies (like singular gigabytes in source size, so hundreds of megabytes in columnar, updated daily), this dance might not be worth it compared to daily full snapshots.
I wish you were right though, my life would be hella easier.
Data providers usually wait for a day or something worth of data to collect before validating and releasing it to customers.
For integrating some OLTP database updating in real time on the other hand, yes you will need CDC.
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Most of data engineering is just incrementally adding new data to existing corpus and then running a big batch job to dedup, sort or partition. This last step surely is computationally expensive, but at least it is conceptually simple and can be solved by throwing hardware at it. The first part of incremental updates is what imo causes more troubles.