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.

EasyTechnical

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Explain when you would use metrics, logs, traces, and lineage data respectively for troubleshooting a high-latency stage in a data pipeline. Provide a concrete investigative workflow that starts from an alert about rising latency and ends with root cause identification.

MediumSystem Design

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Design a mechanism to detect and safely reprocess late-arriving records that affect daily aggregates in a data warehouse. Requirements: ensure idempotence, track reprocessing impact, and minimize compute/cost. Describe components, orchestration steps, and a sample idempotent merge strategy.

MediumTechnical

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Write a PromQL expression (or pseudo-PromQL) that detects a steady increase in end-to-end pipeline latency sustained over 15 minutes rather than a transient spike. Explain why your expression captures trends and how you would tune it to reduce false positives.

EasyTechnical

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List common Kafka metrics you would monitor to detect consumer lag and broker health issues for a real-time pipeline ingesting 100k messages/sec. For each metric briefly state what normal behavior looks like and what would be a concerning signal.

HardTechnical

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Write pseudocode for a distributed streaming job (Flink or Spark Streaming style) that computes per-tenant latency percentiles (e.g., p50, p95, p99) for pipeline latency in near real-time across millions of tenants. Explain state partitioning, use of approximate data structures (t-digest/HDR histogram), and how to merge partial aggregates at query time.

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