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
33 practiced
Describe the difference between partitioning and sharding in the context of data storage and processing. Explain how partition key selection affects query performance and data locality, and give concrete examples of partitioning strategies for Hadoop/Hive tables, Kafka topics, and distributed transactional databases.
MediumTechnical
39 practiced
Explain how you would detect and remediate hot partitions in Kafka when a small set of keys causes producer or consumer imbalance. Include what metrics you would monitor, when to consider a custom partitioner, and how to backfill or rebalance data safely.
MediumTechnical
31 practiced
Describe how to implement schema evolution for Avro and Parquet datasets in a data lake to support backward and forward compatibility. Include a description of tooling (schema registry), contract checks in CI, runtime visitor-safe reads, and strategies for rolling out breaking changes with minimal consumer disruption.
MediumTechnical
40 practiced
A Spark job joins a large events dataset with a small lookup table but tasks are skewed on some keys, causing long-running tasks. Describe how you would profile the job, identify the skew sources, and apply at least three concrete mitigations such as salting, broadcast joins, or repartition strategies.
HardTechnical
36 practiced
As a senior data engineer you must prioritize between (A) rewriting a brittle pipeline to improve reliability, (B) adding new analytics features that drive revenue, and (C) reducing monthly cloud costs. Describe a decision framework with metrics you would use to decide priorities, how you'd phase the work, and how you'd communicate trade-offs to stakeholders.
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