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.

MediumTechnical

0 practiced

Implement a function in Python that, given a directed acyclic graph representing dataset lineage (adjacency list of upstream -> downstream) and a failed node id, returns all downstream datasets affected by the failure. Your implementation should handle graphs of up to 100k nodes efficiently and avoid recursion depth limits.

MediumSystem Design

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Design an observability architecture for a hybrid data platform (batch + streaming) that instruments producers, stream processors, batch jobs and consumers. Requirements: support 10k pipeline jobs, ingest 500k events/sec, retain metrics for 90 days, support trace and lineage queries for debugging, and route alerts to different teams. Sketch high-level components, storage options, and key trade-offs.

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.

HardTechnical

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Your observability bill has grown to 30% of platform costs due to high-cardinality custom metrics and full-fidelity logs. Propose a prioritized technical and organizational plan to reduce costs by 50% over 6 months without losing critical debugging capability. Include short-term wins and longer-term platform changes.

HardTechnical

0 practiced

You're the principal engineer owning observability for data pipelines across multiple teams. Draft a 12-month roadmap to mature monitoring and observability across instrumentation coverage, SLO adoption, alert reduction, lineage coverage, dashboards, and team enablement. Include milestones, measurable success metrics, and a plan to drive adoption across teams.

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