Data Engineering
Streaming (Kafka & Flink)
6 practice questions. Free questions open a full answer guide; the rest unlock with Pro.
- You're designing the topic layout for a high-volume Kafka stream and the team wants both strict ordering and high throughput. Walk me through how you'd choose the number of partitions and the partition key, and the trade-off you're making.
- In a streaming pipeline that aggregates events into time windows, what's the difference between event time and processing time, and how do watermarks let you handle late and out-of-order data?Go Pro
- A consumer reading from Kafka occasionally processes the same message twice after a restart or rebalance. Walk me through how you'd get exactly-once behaviour end to end.Go Pro
- Your team runs a Kafka-to-Flink streaming pipeline that writes aggregates to a downstream store, and occasionally a failure causes either duplicate or missing records in the output. How do you reason about achieving exactly-once end to end, and where are the hard parts?Go Pro
- In a streaming pipeline, what is the difference between event time and processing time, and why do watermarks matter for late-arriving data?Go Pro
- In Kafka, what is a partition, and how do partitions relate to consumer groups and ordering?Go Pro
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