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.
MediumSystem Design
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Design a monitoring and alerting plan to detect performance regressions in data pipelines. Specify key dashboards, SLOs, metrics to collect (ingest rate, processing lag, p95/p99 latencies, queue depth), alert thresholds and severity levels, and runbook steps operators should follow to remediate performance regressions quickly.
MediumTechnical
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Create a benchmarking plan to validate that an ingestion pipeline can sustain 500k events/sec with 20% headroom. Specify how you would generate synthetic data (payload size distribution, event key skew), which metrics to capture (throughput, p95/p99 latency, CPU/Memory/Disk/Network), workload shapes (steady, burst, spiky), and how to incorporate fault-injection such as broker/node terminations or network delays.
HardSystem Design
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Architect a data ingestion and processing platform for a global ad-tech client that receives 50M events/sec peak, requires sub-second tail latency for bidding decisions, and must support both stateful stream processing for real-time features and offline storage for training. Provide a detailed component architecture (ingest tiers, transport, real-time engine, cold storage), partitioning and shard mitigation strategies, state management approach, replication model, autoscaling approach, and operational considerations like SLOs and runbooks. Explain major trade-offs and cost drivers.
MediumTechnical
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Compare tumbling, sliding, and session windows for sessionization in streaming analytics. For a high-scale environment that sees significant out-of-order events, recommend a windowing approach (including watermark and allowed-lateness settings), and list the operational knobs you'd expose to the client to tune accuracy versus latency.
HardTechnical
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An analytical job repeatedly suffers skewed joins because a small set of join keys are very high-cardinality, causing straggler tasks. As a Solutions Architect, enumerate pipeline-level optimizations (pre-aggregation, broadcast joins, salting), storage-level techniques (bucketing, sort/clustered layouts, bloom filters), and query-level strategies (approximate algorithms), and propose a performance test plan to validate each optimization's effect.
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