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

28 practiced

Describe watermarking in streaming windowed aggregations. Provide a concrete example where event-time watermarking prevents incorrect early aggregations (e.g., late events changing a session count), explain how you would choose allowed lateness, and discuss consequences of too-tight versus too-loose watermark settings on memory and result correctness.

MediumSystem Design

36 practiced

A downstream analytics cluster is creating many small Parquet files (tens of thousands per hour) because many parallel writers flush small batches to S3. As a Solutions Architect, design a file-accumulation and compaction strategy that reduces metadata and PUT costs and achieves target Parquet files around 256MB. Include buffering/windowing semantics, atomic commit patterns, failure handling, and how you would maintain near-real-time availability of data.

HardTechnical

29 practiced

Propose a staged migration plan to move a legacy nightly ETL that processes 10 TB/day into a streaming micro-batch architecture with minimal customer impact. Include phases (discovery, prototyping, dual-write, reconciliation, canary rollouts), dual-write and dual-read patterns, reconciliation metrics, backfill and catch-up strategies, validation checkpoints, rollback criteria, and cost implications during transition.

EasyTechnical

38 practiced

Explain backpressure in streaming systems. As a Solutions Architect, describe three practical mechanisms to handle backpressure when a downstream sink slows down (for example: reactive backpressure protocols, durable intermediate queues, adaptive batching and throttling) and explain operational trade-offs (cost, complexity, data loss risk) for each.

EasyTechnical

33 practiced

Define exactly-once, at-least-once, and at-most-once delivery semantics in pipelines. As a Solutions Architect, explain scenarios where exactly-once end-to-end guarantees are necessary, when at-least-once with idempotent consumers is acceptable, and list practical techniques (idempotent keys, transactions, dedup stores) to achieve each guarantee at scale.

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