A real-time grid pipeline playbook for Heads of Data Platform — Kafka as the event backbone, Flink for stateful stream processing, and the operational discipline that makes the difference between a streaming platform that runs and one that pages.
And the operational decisions are paying for it.
Grid signals are inherently real-time. SCADA tags, PMU samples, AMI events, weather, and market signals all arrive in milliseconds and degrade in value with every minute they sit in a batch window.
Most operators built their data platforms in the era when batch was the answer. The platform is correct for the workloads that existed when it was built. It is wrong for the workloads the grid now demands.
Every grid signal lands in Kafka. SCADA, PMU, AMI, weather, market signals — one ingestion contract, one source of truth, one place every downstream workload subscribes to.
Flink handles the windowed aggregations, anomaly detection features, and joins between streams. Flink jobs are version-controlled, deployed with explicit checkpointing, and monitored with watermark-aware metrics.
Some workloads need exactly-once semantics — settlement-relevant aggregations, regulatory reporting feeds, customer-facing alerts. Others tolerate at-least-once. Choosing per workload keeps the platform fast where it can be and correct where it must be.
Every grid signal lands in Kafka. SCADA, PMU, AMI, weather, market signals.
Flink handles the windowed aggregations, anomaly detection features, and joins between streams. Flink jobs are version-controlled, deployed with explicit checkpointing, and monitored with watermark-aware metrics.
Some workloads need exactly-once semantics. Settlement-relevant aggregations, regulatory reporting feeds, customer-facing alerts.
Migrate the batch use cases that benefit most from real-time first. Build the operational runbook — watermark drift, backpressure, checkpoint recovery — that the on-call team will use at 3 a.m.
If your grid signals are real-time and your pipeline is not, the gap is operational value the AI cannot reach.
Flink's stateful operators and watermark handling are more mature for grid use cases. We have shipped Spark Structured Streaming for simpler workloads.
Yes. Streaming is additive. The existing operational systems remain. Streaming feeds new use cases and gradually replaces the batch outputs that benefit from real-time.
Watermark-aware monitoring, alerting on idle sources, and explicit late-event policies per job. Treat the watermark as a first-class operational metric.
Streaming compute is more expensive per event than batch. The cost is recovered in operational value when the latency matters. We size the streaming layer to the workloads where the latency pays back.
For settlement, regulatory feeds, and customer-facing alerts, it removes a class of duplicate-event bugs that batch reprocessing used to mask. For exploration and analytics, at-least-once is usually enough.
