**Spark:** `from_json` with schema; `explode` for arrays; struct access. **Pandas:** `json_normalize`. **Example:** `df.select(col('data').getItem('nested').getItem('field'))`. **Why:** Explicit schemas; handle missing keys. **Production:** `schema_of_json` for inference;...
Pro-Move: Schema inference + validation. Red Flag: Ad-hoc string parsing for nested JSON.
This easy-level Python/Coding question appears frequently in data engineering interviews at companies like TCS. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark) will help you answer variations of this question confidently.
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.
Spark: from_json with schema; explode for arrays; struct access. Pandas: json_normalize. Example: df.select(col('data').getItem('nested').getItem('field')). Why: Explicit schemas; handle missing keys. Production: schema_of_json for inference; flatten for analytics; retain nested for storage. Cost: Flattening can explode rows.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Python/Coding 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.