Building Scalable Real-Time Event Processing Systems - From Zero to 100 Billion Events

Building Scalable Real-Time Event Processing Systems - From Zero to 100 Billion Events

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Leveraging Kafka Rest Proxy

7 of 26

7 of 26

Leveraging Kafka Rest Proxy

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Building Scalable Real-Time Event Processing Systems - From Zero to 100 Billion Events

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  1. 1 Intro
  2. 2 Successful Patterns in Building Realtime Event Processing
  3. 3 Realtime Events Use Case at DoorDash
  4. 4 Requirements for the New System
  5. 5 The Big Picture
  6. 6 Simplify and optimize event producing
  7. 7 Leveraging Kafka Rest Proxy
  8. 8 Batching with Kafka Rest Proxy
  9. 9 Our Proxy Enhancements
  10. 10 Event Processing with Flink
  11. 11 Challenges of a simple Kafka consumer
  12. 12 Leveraging Flink's layered APIs
  13. 13 Flink Platform at DoorDash
  14. 14 Riviera - Applying FlinkSQL in Feature Engineering
  15. 15 Riviera DSL Example For Store Order Count
  16. 16 Unified Event Format and API to Reduce Frictions
  17. 17 Leveraging Schema Registry for Generic Data Processing
  18. 18 Schema update strategy
  19. 19 Build time schema update automation
  20. 20 Data Warehouse and Data Lake Integration
  21. 21 Working Towards a Self-serve Platform
  22. 22 Snowflake Integration Management UI
  23. 23 Leveraging Stream Processing Framework
  24. 24 Creating Abstractions
  25. 25 Fine-grained Failure Isolation and Scalability
  26. 26 Beyond the Architecture

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