What is stream processing?

Stream processing handles data continuously as it arrives, one event or a small window of events at a time, instead of waiting to collect a batch. A stream processor reads from a message system like Kafka and applies logic to each event within milliseconds or seconds, so results stay current with almost no delay.

How it works

Stream processing runs as continuous computation over an unbounded flow of events, rather than a fixed dataset. Tools like Kafka Streams, Flink, or Spark Structured Streaming group events into windows, tumbling or sliding time slices, and keep running state (a running count, a rolling average) as new events arrive.

Why it matters

Some decisions can't wait for the next scheduled batch: fraud checks on a payment, an alert when a metric crosses a threshold, a live dashboard that updates as events happen. Stream processing trades some of batch processing's simplicity for that low latency.

How OptimaFlo handles data in motion

OptimaFlo doesn't do stream processing today. Every source, including message queues like Kafka, lands through the same scheduled Airflow DAGs as every other pipeline: there's no engine like Flink or Spark Structured Streaming applying windowed logic to data in motion, and Raw, Clean, and Ready all run on that batch schedule.

What OptimaFlo does instead is scheduled batch orchestration: template-based Airflow DAGs that are easy to test, retry, and audit, since every run and every generated SQL change is version controlled. For workloads where a few minutes of latency is fine, which covers most reporting and analytics use cases, that's a simpler system to operate than an always-on streaming cluster.

Related terms

• Batch processing: The scheduled model OptimaFlo uses end to end, from ingestion through Clean and Ready.