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
Discuss how schema evolution can affect pipeline performance and downtime. Consider examples such as adding columns to Parquet files, changing Avro schemas for Kafka producers, and renaming fields. Explain compatibility modes (backward, forward, full), schema registry usage, and rollout strategies to avoid consumer failures and pipeline stalls.
EasyTechnical
0 practiced
Explain idempotency and exactly-once semantics in pipelines. For an ETL job that writes feature vectors to an online store (e.g., DynamoDB or Redis) under at-least-once delivery, how would you design writes to be idempotent? Why is achieving global 'exactly-once' difficult in distributed pipelines?
MediumTechnical
0 practiced
Design a benchmarking and load-test strategy for a streaming pipeline (producer -> Kafka -> stream processor -> feature-store). Describe what to measure (throughput, end-to-end latency, p99, consumer lag, resource utilization), how to generate realistic traffic (key distribution, payload sizes, timing), how to isolate components, and which failure modes to simulate during tests.
HardTechnical
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Design a benchmark to compare ingesting Parquet (columnar) versus Avro (row) into an analytical data warehouse. Specify dataset characteristics (schema, cardinality, nullability), ingestion metrics (MB/s, CPU), query metrics (p50/p95 query latency for typical analytic queries), compression codecs, schema-evolution scenarios, and overall cost measurement strategy.
EasyTechnical
0 practiced
You are designing an online feature ingestion pipeline used by real-time ML inference. The pipeline must support 1,000 requests/sec and the model end-to-end SLA is 50ms. Define clear throughput and latency SLIs and SLOs for this pipeline. Explain which percentiles (p50/p95/p99) you would track, where to instrument (producer, message-broker, stream-processor, feature-store), appropriate measurement windows, and how to translate SLI violations into alerts and runbooks.
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