A memorable failure involved a data pipeline that silently dropped records during a schema migration. Situation: We deployed a change that altered column types; null handling differed between dev and prod. The pipeline completed "successfully" but 12% of records were excluded. Approach: We implemented row-count validation at each stage, added dbt tests for uniqueness and completeness, and created alerting on variance from expected volumes....
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 Thoughtworks. The answer also includes follow-up discussion points that interviewers commonly explore.
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