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Can you give an example of processing nested JSON data using these functions?

Python/Codingeasy0.2 min readPremium

**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;...

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Frequency
Low
Asked at 1 company
Category
179
questions in Python/Coding
Difficulty Split
127E|24M|28H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
TCS
Interview Pro Tip

Pro-Move: Schema inference + validation. Red Flag: Ad-hoc string parsing for nested JSON.

Key Concepts Tested
spark

Why This Question Matters

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.

How to Approach This

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.

Expert Answer
35 words

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.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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