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Module 8 · Caching, Queues, Async WorkDay 08025 min

Stream Processing

Treat data in motion as a first-class citizen.

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Day 080

Stream Processing

Source
queue
Window/Aggregate
service
Sink
datastore
Signal path
Windowed stream processing
Source
queue
flow
Window/Aggregate
service
Window/Aggregate
service
flow
Sink
datastore
Memory hook

Stream Processing: treat data in motion as a first-class citizen

Mental model

absorb bursts before they become outages

Design lens

Tighter watermarks = lower latency, more incomplete data.

Recall anchors
WindowsWatermarksEngines

Why it matters

Stream processing computes over continuously arriving data. Operators (map, filter, aggregate, window) transform the stream; checkpointing makes computation fault-tolerant.

Deep dive

Tumbling windows partition time into fixed buckets; sliding overlap.

Session windows close after inactivity gap.

Watermarks handle late events; trade between completeness and latency.

Demo / scenario

Real-time clickstream aggregation.

  1. Tumbling 1-minute windows by user.
  2. Compute clicks/min per user.
  3. Watermark allows 30s late events.
  4. Output to dashboard via Kafka topic.

Tradeoffs

  • Tighter watermarks = lower latency, more incomplete data.
  • State growth requires checkpoint discipline.
  • Exactly-once requires transactional sinks.

Diagram

Source
Window/Aggregate
Sink
Windowed stream processing.

Mind map

Check yourself

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Sources & further reading

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