I was thinking more specifically of the internal architectures of data processing platforms, especially the categorizations that emerged from the MPP database world. The "shared nothing" architecture has been dominant in databases (and is also the core architecture of Hadoop), designed around "co-locating data and compute". Kafka largely follows that architecture as well, using local disk on the compute nodes as its persistent storage layer.
A lot of new data processing platforms, from Snowflake in the data warehouse world to AWS Athena to Apache Pulsar in the broader data processing world, have moved to decoupled architectures.
Containerization and container management frameworks (e.g. Kubernetes) certainly do change the meaning of "local" storage, will be interesting to see how that plays out.