Data Warehouses are (usually) not optimized for individual inserts, updates, deletes (DMLs). Loading data into DWHs is usually done through copy/ingestion comma

Data Warehouses are (usually) not optimized for individual inserts, updates, deletes (DMLs). Loading data into DWHs is usually done through copy/ingestion commands where aggregated/batched data is copied from blob storage (s3, azure, etc...) to a staging table in the DWH. In a non-append-only scenario, Debezium tracks each source operation (insert, update, delete) from the replication log (oplog, binlog, etc) as an individual operation that's emitted into a kafka topic. How does one efficiently replicate this to a Data Warehouse in an efficient manner? I have not been able to use Debezium as way to replicate to a Data Warehouse for this very reason. At least not without having to resort to very complicated data warehouse merge strategies. Note, there exist Data Warehouses that allow tables to be created in either OLAP or OLTP flavor. I understand that Debezium could easily replicate to an OLTP staging table. But are there any solutions if this isn't an option?