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
0 practiced
Your company needs to reduce monthly ETL costs by 40% while keeping SLAs intact. Outline a plan to identify cost levers and recommend performance-preserving cost optimizations across compute choices, storage lifecycle, data movement, and scheduling. Include quick wins and longer-term investments.
MediumSystem Design
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Design a benchmarking plan to validate pipeline performance and cost before production rollout. Define the metrics to collect, how you will generate synthetic data or replay production traffic, what cluster size experiments to run, and how to ensure results are reproducible and representative of real traffic patterns.
HardTechnical
0 practiced
Design a storage tiering strategy for a petabyte-scale data lake where about 5% of data is hot, 25% warm, and 70% cold. Specify lifecycle policies, compaction frequency per tier, query routing for hot/warm/cold data, and cost vs performance trade-offs for choosing different storage classes.
HardTechnical
0 practiced
Design a backpressure-control mechanism that coordinates across Kafka producers, brokers, and Spark Structured Streaming consumers to gracefully handle bursts without data loss. Describe the control signals, algorithms (e.g., token buckets, feedback loops), how the system surfaces pressure to producers, and how to guarantee durability during slowdowns.
MediumTechnical
0 practiced
You must size a Spark cluster to process 500 TB of daily raw data within a 4-hour SLA for ETL jobs. Walk through a capacity planning exercise: estimate input throughput, shuffle volume, memory per executor, executor count, disk and network bandwidth, and trade-offs you might make to reduce cost while meeting the SLA.
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