You have 90 seconds to verbally explain how you would design a real-time analytics dashboard for a trading firm that displays metrics across millions of transactions.
Your system should: - Ingest 1 million transactions per second - Display metrics updated every second (total volume, PnL, top traders) - Support complex queries (filter by trader, asset class, region) - Provide historical drill-down (last hour, day, week) - Handle ad-hoc analytical queries without impacting real-time stream
Explain your streaming architecture, storage, and query optimization.
How to approach it
- Hint 1
Use Lambda architecture: hot path for real-time, cold path for historical analysis
- Hint 2
Stream processing with Apache Flink or Kafka Streams for windowed aggregations
- Hint 3
Columnar storage (Parquet on S3) for analytical queries with caching layer
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