Design a real-time anomaly detection system for 'add to cart' events
Tests real-time data pipeline design and nuanced anomaly detection. A good answer outlines ingestion (Kinesis), processing (Lambda/Flink), seasonal modeling for 'a drop', and alerting (SNS).
This tests your ability to design a real-time data pipeline and apply sophisticated anomaly detection beyond simple thresholds. A strong answer outlines a four-stage architecture: event ingestion (Kinesis), time-windowed aggregation (Flink), a detection algorithm that accounts for seasonality (ML models or comparing to last week's data), and an alerting mechanism (SNS). The most common red flag is suggesting a static threshold, which ignores natural traffic fluctuations and leads to alert fatigue.
Read the original → aws.amazon.com
- #system design
- #real-time
- #analytics
- #anomaly detection
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