A customer-support operations dashboard reads from Kafka customer-support events through an hourly b
A medium Pipeline Design mock interview question on DataDriven. Practice with AI-powered feedback, real code execution, and a hire/no-hire decision.
- Domain
- Pipeline Design
- Difficulty
- medium
Interview Prompt
A customer-support operations dashboard reads from Kafka customer-support events through an hourly batch on the canvas. Support agents complain the dashboard is stale; their freshness target is under 15 minutes (Tier 2). Hourly batch cannot meet Tier 2; pure streaming would meet it but cost 10-50x more for what is fundamentally a 1-to-15-minute freshness need. Apply the micro-batch pattern this section just taught: replace the hourly Spark batch transform with Spark Structured Streaming or Flink (run with an explicit 1-minute or 5-minute trigger interval). Tag the warehouse mart with slaFreshness < 15min so the contract reflects the micro-batch rhythm. Do not introduce a sub-second streaming engine running in true streaming mode; the consumer accepts up to 15 minutes and a smaller trigger costs 5-10x more.
How This Interview Works
- Read the vague prompt (just like a real interview)
- Ask clarifying questions to the AI interviewer
- Write your pipeline design solution with real code execution
- Get instant feedback and a hire/no-hire decision