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.
EasyTechnical
0 practiced
List the key SLIs, metrics, and alert types you would implement to monitor a production ML data pipeline end-to-end (ingestion -> stream-processing -> feature-store -> serving). For each metric explain why it matters, suggested thresholds or approaches to define them, where to instrument, and how to avoid high-cardinality alert storms.
MediumTechnical
0 practiced
Design a streaming join between click events and user profile updates where click events can arrive late and profile updates can be retracted (deleted or corrected). Describe how you'd handle event-time semantics, watermarks, allowed lateness, retractions, and how to ensure the joined features remain correct for both online and offline training pipelines.
EasyTechnical
0 practiced
Describe common causes of hotspotting in distributed data pipelines (e.g., skewed keys, large object writes, sequential disk access), and list immediate mitigation steps you can take in production to reduce latency and avoid data loss. Also explain long-term strategies to prevent hotspots.
MediumTechnical
0 practiced
Design an idempotent sink for a high-throughput streaming pipeline that writes feature vectors into DynamoDB, given Kafka provides at-least-once delivery. Propose a deduplication approach, idempotency key design, handling of retries and network partitions, and a strategy to clean or expire idempotency metadata to bound storage.
EasyTechnical
0 practiced
Explain the difference between partitioning and sharding in the context of large-scale data pipelines (Kafka topics, distributed databases, and feature stores). Give concrete examples of when to prefer each, and discuss operational implications such as rebalancing cost, ordering guarantees, and storage locality.
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.