**Pipeline:** Throughput, latency, failure rate, SLA attainment. **Infra:** CPU/memory, shuffle spill, GC. **Data quality:** Null rates, schema drift, freshness. **Cost:** Compute hours, storage. **Business:** Records processed, downstream impact. **Tools:** Grafana, Datadog;...
Pro-Move: SLA + business metrics combined. Red Flag: Only infra metrics, no data quality.
This easy-level System Design/Architecture question appears frequently in data engineering interviews at companies like Moonfare. While less common, it tests deeper understanding that distinguishes strong candidates.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
Pipeline: Throughput, latency, failure rate, SLA attainment. Infra: CPU/memory, shuffle spill, GC. Data quality: Null rates, schema drift, freshness. Cost: Compute hours, storage. Business: Records processed, downstream impact. Tools: Grafana, Datadog; PagerDuty. Why: SLOs + alerts on breach. Business metrics for prioritization. Scalability: Cardinality limits; aggregate where possible.
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Analyze My Answer β FreeAccording to DataEngPrep.tech, this is one of the most frequently asked System Design/Architecture interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.