Scenario: Your team is tasked with implementing a recommendation engine that processes user interactions in near real-time. How would you design the pipeline architecture to handle this requirement effectively?
- Amazon Kinesis: Real-time data streaming with serverless architecture
- Apache Kafka + Apache Flink: Stream processing with event time processing
- Apache Spark: Batch processing with micro-batch streaming
- Google Cloud Pub/Sub: Managed message queue with push-pull delivery
Apache Kafka combined with Apache Flink is an effective choice for building a recommendation engine that processes user interactions in near real-time. Kafka serves as a distributed message queue for ingesting and buffering user events, while Flink provides stream processing capabilities with event time semantics, ensuring accurate and timely recommendations based on the latest user interactions. This architecture offers high throughput, low latency, fault tolerance, and scalability, essential for real-time recommendation systems.
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