This one could've gone badly but it didn't, for the wrong reasons.
Start by clarifying requirements: what events, volume, latency, and use cases (analytics, real-time personalization). Then design a scalable ingestion pipeline with a message queue, storage optimized for writes and queries, and discuss trade-offs between batch and stream processing.
Pro tip: Emphasize data partitioning and retention strategies early, as they are critical for scalability and cost. Also, mention how you would handle late-arriving events and ensure exactly-once processing.
Ask about event types, expected volume (events per second), latency requirements, and query patterns (real-time dashboards vs. batch analytics).
Outline components: ingestion API, message queue (e.g., Kafka), stream processing (e.g., Flink), storage (e.g., Cassandra, S3), and query layer.
Design schema for events (user_id, event_type, timestamp, properties) and choose storage based on access patterns (e.g., time-series DB, columnar store).
Discuss partitioning, replication, fault tolerance, and how to handle spikes. Mention backpressure and dead-letter queues.
Compare batch vs. stream processing, consistency vs. availability, and cost vs. performance. Suggest optimizations like sampling or aggregation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.