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
Define an alerting policy for when a pipeline's error budget is being consumed quickly. Include burn-rate thresholds, multi-window burn detection (e.g., 1h vs 7d), severity levels, automated remediation actions (safe defaults), and an escalation/communication plan to stakeholders and on-call.
MediumSystem Design
0 practiced
Design an ETL pipeline that performs nightly transformations over 5 TB of transactional data with minimal impact on the production DB and with the ability to safely rollback the last deploy. Outline components (ingest/CDC, staging, transform, write), an incremental processing strategy to avoid full reloads, schema migration approach, and a rollback plan for both code and data issues.
MediumTechnical
0 practiced
A Flink job maintains hundreds of GB of keyed state. Explain strategies to optimize the state backend, including choosing RocksDB vs in-memory state, incremental checkpointing, snapshot frequency, compacting state, tuning RocksDB options, and balancing checkpoint overhead against recovery time objectives.
MediumSystem Design
0 practiced
Design replication and failover strategy for a distributed commit log (Kafka-like) deployed across three availability zones. Requirements: tolerate a single AZ failure without data loss, minimize cross-AZ traffic for normal ops, and keep failover time under 60s. Discuss replica placement, leader election, in-sync replica configuration, client read/write strategies, and trade-offs.
HardTechnical
0 practiced
Discuss the trade-offs of implementing exactly-once processing semantics in distributed data pipelines. Compare approaches: idempotent sinks, Kafka transactions, distributed snapshots/checkpoints, and two-phase commit. For each method, explain operational complexity, latency/cost impact, and situations where at-least-once with idempotency is a better choice.
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