Streaming pipelines and operational KPIs so dispatch and customer teams see delays and exceptions within minutes, not overnight batches.
Dispatch and customer teams were flying blind until the nightly batch landed. We connected shipment and exception events to streaming pipelines and operational dashboards so teams could act while freight was still moving.
How we approached it
Stream topology
Kafka topics partitioned for scale, with stream processing for joins, windows, and late-arriving events.
SLA-driven monitoring
Freshness and lag alerts tied to SLOs; DLQ handling and replay paths documented for on-call.
Operational BI
Role-specific views for hubs and linehaul with drill-down to problematic legs without leaving the ops tool.
What we delivered
- Kafka-based ingestion with stream processing for shipment events
- Operational BI with alerting tied to freshness SLOs
- Observability dashboards for lag, backlog, and DLQs
- Incident runbooks for replay and partial failure
Outcomes
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Minutes-to-insight instead of next-day batch for critical lanes
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Shared vocabulary between dispatch, CS, and engineering
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Foundation for ETA and exception models on the same event fabric
Representative stack: