This one sprawls fast if you're not careful.
Start by clarifying requirements: event types, scale (events per second), latency for near-real-time metrics, and retention period for raw events. Then propose a high-level architecture with separate ingestion, stream processing, and storage layers, and discuss trade-offs between latency, cost, and complexity.
Pro tip: Emphasize the importance of idempotency and exactly-once processing to avoid double-counting metrics, and mention how you would handle late-arriving events with watermarks or windowing.
Ask questions to understand event volume, required latency for dashboards, data retention, and query patterns. This shapes the entire design.
Propose a scalable ingestion endpoint (e.g., API gateway + load balancer) that writes events to a durable, high-throughput message queue like Kafka or Kinesis.
Use a stream processor (e.g., Flink, Spark Streaming, Kafka Streams) to aggregate events in near-real-time and write results to a fast-access store like Redis or a time-series database for dashboards.
Persist raw events to a data lake (e.g., S3, HDFS) or a columnar store (e.g., Parquet on S3) for offline batch analysis, ensuring durability and cost-effectiveness.
Discuss trade-offs: latency vs. cost, exactly-once vs. at-least-once, and how to handle failures, scaling, and data consistency across layers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.