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How do you handle schema mismatches during merging?

System Design/Architectureeasy0.2 min readPremium

**Strategies**: (1) Schema evolution—additive; defaults; (2) unionByName(allowMissingColumns=True) in Spark; (3) Mapping—explicit column mapping; null for missing; (4) Validation—quarantine incompatible; (5) Versioning—tag with schema version....

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Frequency
Low
Asked at 1 company
Category
179
questions in System Design/Architecture
Difficulty Split
15E|6M|158H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Virtusa
Interview Pro Tip

Pro-Move: 'Two sources added column at different times. We use unionByName; new columns default null. Backfill job filled historical.'

Key Concepts Tested
spark

Why This Question Matters

This easy-level System Design/Architecture question appears frequently in data engineering interviews at companies like Virtusa. 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

Strategies: (1) Schema evolution—additive; defaults; (2) unionByName(allowMissingColumns=True) in Spark; (3) Mapping—explicit column mapping; null for missing; (4) Validation—quarantine incompatible; (5) Versioning—tag with schema version. Prefer additive; coordinate with producers; test with samples from all sources.

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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According to DataEngPrep.tech, this is one of the most frequently asked System Design/Architecture 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.

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