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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.

MediumSystem Design
0 practiced
You manage observability for a platform with ~100k datasets. Design a lineage and impact-analysis solution that answers the question: 'Which downstream tables and dashboards are impacted if table X is bad?' Include metadata model (nodes/edges attributes), storage choices (graph DB vs relational), and query patterns to keep responses under a second for common queries.
MediumTechnical
0 practiced
Compare options for telemetry storage: Prometheus for short-term metrics, Thanos/Cortex for long-term metrics, ELK for logs, and Tempo/Jaeger for traces. For each option discuss retention, query latency, cost profile, cardinality constraints, and typical use cases within a data pipeline observability architecture.
EasyTechnical
0 practiced
Name three common alert categories for production data pipelines (for example: job failures, lag/backpressure, data quality regressions). For each category: provide a concrete alert definition, a reasonable threshold example, and the first three runbook steps an on-call engineer should take.
HardSystem Design
0 practiced
Design a monitoring and auditing blueprint for GDPR-sensitive data pipelines that must record access events and errors without exposing PII in logs, metrics, or traces. Include redaction/tokenization strategies, cryptographic hashing approaches, key management considerations, and how to enable lawful access for investigations while maintaining privacy.
HardSystem Design
0 practiced
Design per-tenant observability for a multi-tenant data platform that ensures tenant isolation, cost attribution, and mitigation of noisy tenants. Describe tagging and telemetry partitioning strategies, quota enforcement, per-tenant dashboards, and how to implement cost-based alerting that notifies tenants when their telemetry usage approaches limits.

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