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

56 practiced

Discuss batching and windowing trade-offs when designing aggregations for BI dashboards. Explain how batch size, window duration, and allowed lateness influence latency, resource utilization, correctness of metrics, and the freshness of executive reports. Include examples where larger batches improve efficiency but increase staleness for stakeholders.

MediumSystem Design

39 practiced

Design a monitoring and alerting strategy a BI team can operationalize for pipeline performance and data-quality issues. Include the key dashboards (ingest rate, backlog, p99 transform latency, failed-run rate), alert thresholds and escalation paths, and a concise playbook for actions to take on slow jobs, growing backlogs, and missing upstream data.

EasyTechnical

31 practiced

Describe the practical differences between batch and streaming data pipelines from the perspective of a BI analyst building dashboards and reports. Include typical latency and throughput ranges, operational complexity, cost implications, and examples of BI use cases best suited to each approach (e.g., nightly ETL for accurate aggregates vs event-driven streaming for near-real-time KPIs).

MediumTechnical

32 practiced

Given a Postgres table events(user_id bigint, occurred_at timestamptz, event_type text), write a SQL query that computes daily 7-day rolling Active Users (unique users in the previous 7 days) for each day in the past 90 days. Return columns: day (date), seven_day_active (integer). Explain scaling considerations for very large tables.

HardTechnical

35 practiced

As a BI lead, you must convince engineering and finance to invest in pipeline upgrades to meet a 10x throughput SLA. Draft an approach for presenting technical trade-offs, a cost-benefit analysis, the key performance indicators that will demonstrate improvement, and a pilot plan to validate ROI with minimal disruption to production.

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