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Flink Platform at DoorDash
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Classroom Contents
Building Scalable Real-Time Event Processing Systems - From Zero to 100 Billion Events
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- 1 Intro
- 2 Successful Patterns in Building Realtime Event Processing
- 3 Realtime Events Use Case at DoorDash
- 4 Requirements for the New System
- 5 The Big Picture
- 6 Simplify and optimize event producing
- 7 Leveraging Kafka Rest Proxy
- 8 Batching with Kafka Rest Proxy
- 9 Our Proxy Enhancements
- 10 Event Processing with Flink
- 11 Challenges of a simple Kafka consumer
- 12 Leveraging Flink's layered APIs
- 13 Flink Platform at DoorDash
- 14 Riviera - Applying FlinkSQL in Feature Engineering
- 15 Riviera DSL Example For Store Order Count
- 16 Unified Event Format and API to Reduce Frictions
- 17 Leveraging Schema Registry for Generic Data Processing
- 18 Schema update strategy
- 19 Build time schema update automation
- 20 Data Warehouse and Data Lake Integration
- 21 Working Towards a Self-serve Platform
- 22 Snowflake Integration Management UI
- 23 Leveraging Stream Processing Framework
- 24 Creating Abstractions
- 25 Fine-grained Failure Isolation and Scalability
- 26 Beyond the Architecture
