I worked for a high profile recently-failed project from a company that rhymes with Brillo, and our data was just beginning to be too big for google sheets (!). However, we were also having organizational problems because the higher ups were seeing the failing project losing money so they of course decided to hire 100 extra engineers. Our communications (both human and programmatic) were failing and the confluent salespeople began circling like buzzards. Of course by the time it was suggested we we use it the project was already 6 months past the point of no return.
My advice is that if your data fits in a database, use a database. Anyone who says that isn’t scalable should have to tell you the actual reason it doesn’t scale and the number of requests/users/GBs/uptime/ etc that is the bottleneck.
Kafka sits at roughly the same tier as HTTP, but lacks a lot of the convention we have around HTTP. There's a lot of convention around HTTP that allows people to build generic tooling for any apps that use HTTP. Think visibility, metrics, logging, etc, etc. Those are all things you effectively get for free with HTTP in most languages. Afaict, most of that doesn't exist for Kafka in a terribly helpful. You can absolutely build something that will do distributed tracing for Kafka messages, but I'm not aware of a plug-and-play version like there are for most languages.
The fact that Kafka messages are effectively stateless (in the UDP sense, not the application sense) also trips up a lot of people. If you want to publish a message, and you care what happens to that message downstream, things get complicated. I've seen people do RPC over event buses where they actually want a response back, and it became this complicated system of creating new topics so the host that sent the request would get the response back. Again, in HTTP land, you'd just slap a loadbalancer in front of the app and be done. HTTP is stateful, and lends itself to stateful connections.
Another issues it that when you tell people that they can adjust their schema more often, they tend to go nuts. Schemas start changing left and right, and suddenly you now need a product to orchestrate these schema changes and ensuring you're using the right parser for the right message. Schema validation starts to become a significant hurdle.
It's also architecturally complicated to replace HTTP. An HTTP app can be just a single daemon, or a few daemons with a load balancer or two in front. Kafka is, at minimum, your app, a Kafka daemon, and a Zookeeper daemon (nb I'm not entirely sure Zookeeper is still required). You also have to deal with eventual consistency, which can make coding and reasoning about bugs dramatically harder than it needs to be. What happens when Kafka double-delivers a message?
My pitch is always that you shouldn't use Kafka unless it becomes architecturally simpler than the alternatives. There are problems to which Kafka is a better solution than HTTP, but they don't start with unstable schemas or databases being difficult. Huge volumes of data is a good reason to me, not being sure what your downstreams might be is an option. There are probably more, I'm not an expert.
> our customers don't understand the data they're shoving at us. But Kafka will take care of all of that for us
Kafka isn't going to help with this at all. If your HTTP app can't parse it, neither will your Kafka app. Kafka does have the ability to do replays, but so does shoving the requests in S3 or a databases for processing later. I promise you that "SELECT * FROM requests WHERE status='failed'" is drastically simpler than any Kafka alternative. It is neat that Kafka lets you "roll back time" like that, but you have to very carefully consider the prospect of re-processing the messages that already succeeded. It's very easy to get a bug where you have double entries in databases or other APIs because you're reprocessing a request.