Stream Processing and Real-Time Analytics

Stream processing turns event flow into continuous computation rather than treating events only as integration messages. Once teams start computing rolling counts, joins, alerts, and dashboards from live streams, they also inherit new responsibilities around state, time, replay, and operational safety.

Read the lessons in order. The first separates stateless from stateful stream processing and explains why the distinction matters. The second covers windowing, aggregation, and joins, which is where time becomes a modeling decision rather than only a timestamp field. The third focuses on replay, reprocessing, and backfills. The fourth turns to real-time dashboards and alerting, where analytics and operational monitoring often converge on the same event flow.

If your platform already has streams but nobody can explain how state, time, and replay affect the correctness of analytics or operational views, Chapter 13 is where that model needs to become explicit.

In this section

• Stateless vs Stateful Stream Processing
  A practical lesson on the difference between per-event transformation and stateful continuous computation, including why state introduces stronger correctness and operations obligations.
• Windowing, Aggregation, and Joins
  A practical lesson on stream-time grouping, rolling metrics, and joining event streams, including why time semantics must be explicit in continuous analytics.
• Replay, Reprocessing, and Backfills
  A practical lesson on rebuilding analytics and derived views safely from historical streams, including side-effect isolation, correction workflows, and replay controls.
• Real-Time Dashboards and Alerting
  A practical lesson on using event streams for live operational visibility, including metric freshness, alert design, and the trade-off between timeliness and reliability.