Ultimately, this comparison is apples vs oranges...
Never mind that the persistent durable log pattern of kakfa enables a lot replay type use cases that are very beneficial in financial systems specifically.
It’s not solely because of hype at all, it’s objectively better for many use cases.
Traditional message brokers (RabbitMQ and similar) do support the event-driven architecture, yet the data they handle is ephemeral. Once a message has been processed, it is gone forever. Connecting a new raw data source is not an established practice and requires a technical «adapter» of sorts to be built. High concurrency levels is problematic for scenarios where the strict message processing ordering is required: the traditional message brokers do not handle it well in highly parallel scenarios out of the box.
Kafka and similar also support event-driven architectures, yet they allow the data to be processed multiple times – by existing (i.e. a data replay) and, most importantly, new or unknown at the time consumers (note: this is distinct from the data replay!). This is allows to plug existing data source(s) into a data streaming platform (Kafka) and incrementally add new data consumers and processors over the time with the datasets being available intact. This is an important distinction. Kafka and similar also improve on the strict processing order guarantee by allowing a message source (a Kafka topic) to be explicitly partitioned out and guaranteeing that the message order will be retained and enforced for a consumer group receiving messages from that partition.
To recap, traditional message brokers are a good fit for handling the ephemeral data, and data streaming platforms are a good fit for connecting data sources and allowing the data to be ingested multiple times. Both implement and support event-driven architectures in a variety of scenarios.