**Architectural Logic**: Null counting is a data quality primitive; automation scales governance. **SQL**: `SELECT SUM(CASE WHEN col1 IS NULL THEN 1 ELSE 0 END) AS col1_nulls, ... FROM table`. Or `COUNT(*) - COUNT(col1)` (when NULL excluded from COUNT). **BigQuery**: `COUNTIF(col1 IS NULL)`. **Spark**: `df.agg(*[sum(when(col(c).isNull(), 1).otherwise(0)).alias(c) for c in df.columns])`. **Why Automate**: For 50+ columns, dynamic SQL or schema iteration; run as part of CI/data quality framework....
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 Incedo. The answer also includes follow-up discussion points that interviewers commonly explore.
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