Can you answer how a given event came to be? Is it an aggregation, a combination of multiple events with different sources? What are its origins?
Given the growing complexity of event streaming architectures - stateful processing, joins, fan-outs, multi-cluster flows - it is increasingly important to be able to accurately answer those questions, understand data flows and capture data provenance.
This talk will walk through how to use and extend OpenTelemetry Java agent auto instrumentation to achieve full end-to-end traceability in Kafka event streaming architectures involving multi-cluster deployments, the Connect platform, stateful KStream applications and ksqlDB workloads.
We will cover: