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
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.
MediumTechnical
0 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
0 practiced
Write pseudocode for a capacity-aware autoscaler that scales streaming consumer pods based on a combination of consumer lag, CPU utilization, and cost. The algorithm should prioritize meeting latency SLAs, avoid flapping (provide cooldown logic), and consider warm-up time for new pods. Outline parameters, thresholds, and how state is persisted between evaluation intervals.
HardTechnical
0 practiced
You operate Flink jobs with TB-scale keyed state. During scaling operations state redistribution causes long rebalance times and latency spikes. Propose architectural and operator-level solutions (incremental checkpoints, state co-location, operator fusion/chaining, warm standby tasks) to enable low-impact scaling. Explain benefits and trade-offs of each approach.
EasyTechnical
0 practiced
You operate a streaming cluster that sees predictable traffic spikes every morning between 08:00-09:00. Describe a simple autoscaling strategy (scheduled, reactive, or hybrid) to handle these spikes that balances cost and performance. Specify which metrics you would scale on, cooldown periods, and any pre-warming or schedule-based optimizations.
Unlock Full Question Bank
Get access to hundreds of Data Pipeline Scalability and Performance interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.