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Real-time processing

As explained in Kafka Streams in Action, Second Edition

Real-time processing is the processing of data with sufficiently low delay to support decisions while events are occurring.

Real-time processing handles event data with sufficiently low delay to support decisions while events are occurring. An event-streaming platform captures events, transfers and stores them in order, and lets applications process or analyze the resulting stream. section 1.1

Use real-time processing when immediate responses matter, including:

  • Reviewing purchases as they happen to detect potentially fraudulent credit-card use. section 1.1
  • Interpreting temperature-sensor signals while a manufacturing problem may be developing. section 1.1
  • Sending routing information to the appropriate department as business events arrive. section 1.1

Replaying a sudden sales surge

After an unusually successful online sale crashes servers, engineers replay the durable event history to understand the influx and improve capacity for future activity. section 1.1

How Kafka Streams in Action, Second Edition covers it

How deep it goes
Explains low-delay processing, ordered storage, analysis, and replay, but does not detail implementation APIs or deployment mechanics.
Examples
Uses credit-card purchases, manufacturing temperature sensors, business routing, and an e-commerce sale surge.
What you can do after
Evaluate incoming events promptly and use stored event histories to investigate outcomes and improve future handling.
How it connects
Connects event streams with event-streaming platforms, durable storage, event replay, fraud detection, and sensor monitoring.

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This explainer was generated by Manning's knowledge graph from the text of Kafka Streams in Action, Second Edition. The section links point to the parts of the book it draws on.