A lot of groups start using Kafka because they have high-throughput event streams they want to aggregate over, and then you just use Kafka for everything because managing a 5 TPS topic alongside a 100k TPS topic is trivial.
In terms of why Kafka is a good fit for that workflow — database writes are unnecessary and oddly structured for raw events we want to expire in a few days, and dealing with buffering blob file writes can cause data loss, so Kafka can really simplify the producer architecture where the producer is also a consumer from a producer who wants an ack. Combine this with how trivial it is to have multiple readers on a pub sub system, and it is easy to scale from 1 to N consumers of a data source without duplicating the data everywhere. E.g. you could have three aggregation jobs that use the same data, one job that writes the topic’s data to blob-style storage for batch use-cases, and a low latency inference job all running from the same data stream.
More or less, Kafka just simplifies scale-out in some cases, maybe not your case, though. If you’re kicking off workers to do ingestion, you might have a system where you are pulling files down at some infrequent cadence (Let’s say every 10 minutes) —- in that case Kafka is likely going to be overkill and feel like a lot of work for an API call that then becomes a tasked tracked in some database.