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

Describe how distributed tracing can be used to debug inter-service data pipelines where an event flows through producer, stream processor, batch job, and downstream API. What key spans and tags would you ensure are present? Which sampling strategy would you choose for production and why?

MediumTechnical

0 practiced

Design an approach to correlate business-level metrics (e.g., orders/sec) with pipeline-level telemetry to detect when pipeline degradations affect business KPIs. Describe instrumentation, dashboards, and alerting rules that map technical failures to business impact.

EasyTechnical

0 practiced

Provide a short Python example using the Prometheus client library that instruments a streaming worker with: a) a counter for processed events, b) a histogram for processing latency in milliseconds, and c) a gauge for current in-flight tasks. Include labels for pipeline_name and stage.

HardSystem Design

0 practiced

Design an automated recovery playbook for failed bulk-load jobs that includes detection, safe retry, idempotency guarantees, deduplication strategies, and escalation. Describe how to make retries safe for side-effecting sinks (e.g., external APIs) and how to test the playbook.

MediumTechnical

0 practiced

You observe that an alert on high error rate is firing frequently but most pages are resolved by restarting a downstream worker. As an SRE, how would you investigate to find the root cause and what metrics or telemetry would you add to avoid relying on restarts as the default fix?

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