I went with product recommendations for batch (run nightly, serve from a cache) and fraud detection for real-time.
Define batch and real-time processing, then provide a concrete use case for each, explaining why the latency and throughput requirements make one suitable over the other. Highlight the trade-offs and how the choice impacts system design and business metrics.
Pro tip: Tie each use case to a business metric (e.g., cost savings for batch, user engagement for real-time) to show product thinking, and mention that some systems use a hybrid approach (lambda architecture) to balance both.
Briefly explain that batch processing handles large volumes of data offline with high throughput and latency tolerance, while real-time processing requires low-latency predictions on streaming data.
Give a specific example, such as nightly recommendation model training or daily fraud detection report generation, and explain why latency is not critical and how batch efficiency reduces cost.
Give a specific example, such as real-time ad targeting or instant credit card fraud detection, and explain why sub-second latency is essential for user experience or security.
Compare the two in terms of infrastructure (e.g., Hadoop/Spark vs. Kafka/Flink), cost, complexity, and freshness of data, and mention how the choice affects model updating and monitoring.
Summarize how each approach aligns with different business needs and metrics, and optionally mention hybrid architectures like lambda or kappa for combined requirements.
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