If the postgres database is recording business transactions, you don't want to cause your business to stop being able to take credit cards because you generated a report.
Futhermore, Postgres is an OLTP (transactional) database, designed to efficiently perform updates and deletes on individual rows. OLAP (analytical) databases/query engines like Clickhouse, Presto, Druid, etc. are designed for efficient processing across many rows of mostly unchanging data.
Analytical queries (like "find the average sales price across all orders over the past year, grouped by store and region") can be 100-1000x faster in an OLAP database compared to Postgres.
That’s the use case for cdc, to make it equally easy to use a DW. As always the complexity is just air you move in the balloon. The oltp db can spit out the events and forget them, how you load them efficiently is now a data engineer’s problem to solve ( if it was easy to write event grain on an olap you would not need an oltp). Kafka usually enters the room at this stage and the simplification promise is becoming tenuous.
