A benefit of Kafka is that it is relatively trivial to spin up multiple consumers from a single CDC stream. If you have some user attribute table that you set

A benefit of Kafka is that it is relatively trivial to spin up multiple consumers from a single CDC stream. If you have some user attribute table that you set up with Debezium, there are a couple downstream consumers (teams!) that also want this data. So you hook up the table with debezium to Kafka and write all changes to a Kafka log. A team of data scientists can stream these straight into their model, or a team of data engineers can dump these changes real time into an analytics store for business intelligence, or another team that needs access to this data can also create their own denormalized table of user attributes as well. For the data producing team, once they get their data into Kafka basically any team can consume from this data which is a lot easier to maintain for the producing team and does not stress the database so its a very nice pattern for bigger orgs. For the most part, keeping the data in a single postgres instance as long as possible would be my recommended solution. But there is a point where getting the data to consumers of that data becomes tedious enough that this additional infrastructure is worth it.