**Section 1 — The Context (The 'Why')**
Data quality in pipelines requires validation, monitoring, and remediation. The primary challenge is detecting issues without blocking and tracing impact. Silent drops cause undetected data loss....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like American Express. The answer also includes follow-up discussion points that interviewers commonly explore.
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