Data Pipeline Scalability and Performance Questions

Design data pipelines that meet throughput and latency targets at large scale. Topics include capacity planning, partitioning and sharding strategies, parallelism and concurrency, batching and windowing trade offs, network and I O bottlenecks, replication and load balancing, resource isolation, autoscaling patterns, and techniques for maintaining performance as data volume grows by orders of magnitude. Include approaches for benchmarking, backpressure management, cost versus performance trade offs, and strategies to avoid hot spots.

MediumTechnical

30 practiced

During a high-traffic window you receive alerts: consumer lag increasing sharply and p99 processing latency climbing. Walk through your immediate on-call triage steps, short-term mitigations to avoid SLA breaches (without data loss), and actions to bring the pipeline back to healthy levels. Include which dashboards and logs you'd prioritize.

HardTechnical

30 practiced

A third-party API used in your enrichment step slowed significantly and caused pipeline queues to grow, backpressure, and partial data loss after retries exhausted. Describe an incident response plan: immediate containment (what to throttle/stop), communication to stakeholders, short-term mitigations (circuit-breaker, fallback data or cached enrichment), and long-term fixes (alternate enrichment sources, SLA negotiation).

EasyTechnical

37 practiced

Explain the purpose of partitioning/sharding in data pipelines. Describe at least three partitioning strategies (key-hash, range, round-robin), the trade-offs each makes regarding load balancing, data locality, rebalancing cost, and query patterns, and one scenario where each strategy is a good fit.

MediumTechnical

39 practiced

Your Kafka topic is keyed by user_id, but a small percentage of users generate 80% of events, causing severe hotspotting on partitions. Design a partitioning and operational strategy to mitigate hotspots while preserving per-user ordering where needed. Evaluate options like dedicated hot partitions, key-salting with ordered shards, separate topics for heavy hitters, and dynamic routing.

EasyTechnical

32 practiced

Compare tumbling windows, sliding windows, and session windows in streaming frameworks. For each window type provide a canonical analytics use case (e.g., per-minute metrics, rolling averages, user session detection) and explain how you would handle late and out-of-order events (watermarks, allowed lateness).

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