Design a scalable system to collect, aggregate, and report real‑time product usage metrics across multiple product lines, ensuring data integrity, low latency, and support for A/B testing and feature flagging.
onsite · 3-5 minutes
How to structure your answer
Use CIRCLES to define requirements, then outline a data ingestion pipeline (Kafka), storage (data lake + time‑series DB), processing (Spark/Stream), and API layer (GraphQL). Detail scaling (partitioning, sharding), data quality checks, and monitoring. Conclude with a deployment strategy (CI/CD, blue‑green).
Sample answer
First, clarify business goals: real‑time insights, low latency, and support for A/B testing and feature flags. Use CIRCLES to break down requirements: Collect, Ingest, Store, Process, Query, Alert, and Iterate. Design the ingestion layer with Kafka to handle high‑volume event streams, applying schema registry for data consistency. Store raw events in a data lake (S3) and materialize aggregates in a time‑series database (InfluxDB) for fast queries. Process data with Spark Streaming for batch‑like transformations and Kafka Streams for low‑latency alerts. Expose metrics via a GraphQL API, enabling product teams to query usage and A/B test results. Implement CI/CD pipelines for schema changes, automated tests, and blue‑green deployments. Add monitoring with Prometheus and Grafana to track ingestion lag, query latency, and error rates. This architecture scales horizontally, ensures data integrity, and supports rapid experimentation.
Key points to mention
- • Data ingestion pipeline design
- • Schema evolution strategy
- • Real‑time aggregation and alerting
Common mistakes to avoid
- ✗ Overengineering the architecture with unnecessary services
- ✗ Ignoring data quality and schema validation
- ✗ Neglecting latency requirements for real‑time use cases