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Data processing with Kafka Streams and Spring Framework.

Talkat Devoxx Ukraine 2020 · 2020-11-05..11-07· [java]
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Builds a three-part system: a Python Raspberry Pi sensor (mocked in demo) → REST service → Kafka topic consumed by a Kafka Streams microservice that computes 5‑second windowed averages and emits alerts when thresholds are exceeded. Uses Kafka Streams on top of Kafka, the Spring Cloud Stream Kafka Streams binder, and local state in RocksDB backed by changelog topics. Demonstrates exactly‑once processing (processing.guarantee=exactly_once), shows late records can be dropped because time is tracked per partition, and the demo runs end‑to‑end.

AI-written summary · about

  1. 0:00Introduction
  2. 1:19About the speaker
  3. 2:30Origin of Kafka name
  4. 3:30Agenda overview
  5. 5:46What is Kafka Streams
  6. 7:19Use cases
  7. 10:08Core features
  8. 12:33Processor topology
  9. 15:15Microservices integration
  10. 17:56Spring Cloud Stream
  11. 19:07Project planning
  12. 22:25Building the REST service
  13. 24:54Data model creation
  14. 27:33Kafka integration
  15. 30:23Kafka producer testing
  16. 34:40Setting up Kafka topics
  17. 38:00Streams app setup
  18. 41:05Windowing concepts
  19. 43:52Live coding aggregations
  20. 47:33State management
  21. 50:20Alert processor logic
  22. 55:23Configuring bindings
  23. 1:01:12Testing the workflow
  24. 1:05:40Configuring transactions
  25. 1:08:42Kafka transactions
  26. 1:12:03Time management
  27. 1:15:36Handling late data
  28. 1:23:33Data skipping issues
  29. 1:26:48Deduplication strategy
  30. 1:29:16Stream time complexity
  31. 1:32:36Exactly-once processing
  32. 1:37:11Real-time commit config
  33. 1:40:07Production deployment
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