Personally, I find Brooklyn Data's post to be more practically useful since they used each format to do real work and show how they perform in practice. And they provide all their code so a reader can run the same testing themselves.
https://brooklyndata.co/blog/benchmarking-open-table-formats
First, you should probably add disclaimer that you work for Databricks.
Second, they really did not.
They came up with a test case of 1) create a table 2) merge into it 3) delete from it 4) delete from it 5) delete from it 6) delete from it (but call it "GDPR request" in chart).
This is a completely absurd workload that should give anyone a pause. In real world, Lakehouse tables are either appended to, merged into with periodic compaction, or dropped and recreated (using something like dbt). Running a delete four times is just bizarre, and making a general statement out of it is just suspiciously biased.
Of course it is very interesting coincidence that this independent company came up with non-realistic benchmark showing how better Delta is compared to major competitor, that everyone from Databricks is sharing everything.
I sense a pattern here. Not sure what makes these tests "absurd" since everyone does them.
I cant paste images here but imo this table comparing the 3 formats is the big takeaway https://assets-global.website-files.com/6064b31ff49a2d31e049... (explained inline, we do cite onehouse heavily but we are independent of them)