Data Pipeline Monitoring and Observability Questions

Focuses on designing monitoring and observability specifically for data pipelines and streaming workflows. Key areas include instrumenting pipeline stages, tracking health and business level metrics such as latency throughput volume and error rates, detecting anomalies and backpressure, ensuring data quality and completeness, implementing lineage and impact analysis for upstream failures, setting service level objectives and alerts for pipeline health, and enabling rapid debugging and recovery using logs metrics traces and lineage data. Also covers tooling choices for pipeline telemetry, alert routing and escalation, and runbooks for operational playbooks.

HardBehavioral

24 practiced

You're the on-call SRE and a high-priority pipeline is failing during peak business hours with risk of data loss. Role-play your first 20 minutes: what commands/metrics do you check, who do you notify, what mitigations do you attempt, and how do you communicate status to stakeholders?

HardSystem Design

28 practiced

Design an end-to-end observability architecture for a high-throughput streaming data pipeline that handles 1M events/sec and requires <5s end-to-end latency. Include telemetry collection (metrics, traces, logs), storage/retention strategy, aggregation tiers, sampling, alerting, and cost-control measures. Discuss trade-offs and capacity planning.

HardTechnical

30 practiced

You rely on several third-party APIs for ingestion. Propose instrumentation and monitoring to detect degradation in data quality from these sources (schema drift, missing fields, increases in nulls). Explain automated vs manual mitigation steps when drift is detected.

HardTechnical

28 practiced

A downstream analytics team requires an SLA that their aggregated daily metrics must be within 0.1% of the true counts. As the SRE, propose how to define, verify, and enforce such an SLA: measurement techniques, sampling-based verification, canary datasets, and consequences when SLA is violated.

MediumTechnical

23 practiced

Write an example Prometheus alerting rule (YAML) that fires a 'HighErrorRate' alert when the 5m increase rate of failed_events_total for pipeline 'transactions' is > 1% of the processed_events_total over the same interval, and stays high for 10 minutes. Include a suggested severity label.

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