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What are the best practices for logging and monitoring bad data?

System Design/Architecturemedium0.3 min read

(1) Structured logging—JSON with record_id, rule_id, severity, sample. (2) Centralized—ELK, Splunk, CloudWatch. (3) Metrics—failed count per rule; alert on spikes. (4) DLQ—store bad records for reprocessing. (5) Dashboards—DQ score, trends. (6) Sampling—log sample, not all. WHY:...

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
Category
179
questions in System Design/Architecture
Difficulty Split
15E|6M|158H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
PWC
Interview Pro Tip

Red Flag: Blocking pipeline on bad data. Pro-Move: 'DLQ + sampled logging; we replay from DLQ after fix; metrics feed into DQ dashboard; 99.9% good data path unaffected.'

Why This Question Matters

This medium-level System Design/Architecture question appears frequently in data engineering interviews at companies like PWC. While less common, it tests deeper understanding that distinguishes strong candidates.

How to Approach This

Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.

Expert Answer
56 words

(1) Structured logging—JSON with record_id, rule_id, severity, sample. (2) Centralized—ELK, Splunk, CloudWatch. (3) Metrics—failed count per rule; alert on spikes. (4) DLQ—store bad records for reprocessing. (5) Dashboards—DQ score, trends. (6) Sampling—log sample, not all. WHY: Root cause without full rerun. SCALABILITY: Sampling and aggregation; avoid log explosion. COST: Log volume = cost; sample and aggregate.

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