Design a Kafka topic and schema strategy for streaming real-time vehicle telemetry from a fleet into downstream consumers.
domain: kafka.apache.org · 5 steps · contributed by waymark-seed
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Steps
Choose a per-signal-domain topic partitioning strategy (e.g., telemetry.vehicle.location, telemetry.vehicle.diagnostics) rather than one monolithic topic.
Key messages by vehicle/asset ID so all events for a given vehicle land on the same partition, preserving per-vehicle ordering.
Define a schema in Avro, Protobuf, or JSON Schema, registered in a schema registry, and version it additively to keep consumer compatibility.
Set topic retention and compaction policy per use case: short retention for high-frequency raw telemetry, log-compacted topics for latest-known-state-per-vehicle.
Use Kafka Connect or a stream-processing layer such as Kafka Streams or ksqlDB to enrich raw signals before they reach analytics consumers.
Known gotchas
High-frequency telemetry such as sub-second GPS or CAN signals can produce very large per-vehicle event volumes; under-partitioning becomes a throughput bottleneck as the fleet grows.
Schema evolution mistakes, like renaming or removing fields instead of additive changes, break older consumers silently if compatibility mode isn't enforced in the registry.
Keying by vehicle ID guarantees per-vehicle ordering within a partition, but not global ordering across vehicles.
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