Apache Kafka
A durable, replayable event log for high-volume streams — the backbone when device telemetry outgrows a simple broker.
In one line
Kafka is a distributed, append-only event log: producers write events to partitioned topics, the log persists them for days or forever, and any number of consumer groups read at their own pace — including re-reading history.
How it works
Unlike a message queue that deletes on delivery, Kafka retains events; consumers
just track their offset. Partitions give parallelism and ordering per key (all events
for device-42 stay in order); replication gives durability; consumer groups give
scale-out. The replay property is the killer feature: deploy a new analytics service
today and let it consume last month's stream as if live.
Where it shows up in digital health
Hospital-scale device telemetry (MQTT at the edge, bridged into Kafka in the core), HL7/FHIR event backbones feeding many downstream systems independently, audit pipelines, and ML feature streams. The honest sizing note from this platform's own cost rules: Kafka earns its operational weight at high volume — do not run it for ten devices.