**Architectural Logic**: Nulls and semi-structured data require policy, validation, and flexible schemas. **Nulls**: COALESCE/IFNULL for defaults; define semantics (missing vs not applicable). Use sentinel values (e.g., -1, 'Unknown') for dimensions; document policy....
This easy-level SQL question appears frequently in data engineering interviews at companies like Capgemini. 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.
Architectural Logic: Nulls and semi-structured data require policy, validation, and flexible schemas. Nulls: COALESCE/IFNULL for defaults; define semantics (missing vs not applicable). Use sentinel values (e.g., -1, 'Unknown') for dimensions; document policy. Unstructured: Schema-on-read (Parquet, JSON); JSON_EXTRACT, from_json for extraction. Validate and handle malformed; optional chaining. Data Quality: Null checks in pipelines; dbt/great_expectations tests; log anomalies. Scalability: Separate columns for 'missing' vs 'N/A'; use struct/variant for flexible fields. Cost: Validate early to avoid propagating bad data.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL 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.