**Strategy**: (1) Schema evolution—additive changes; new columns, defaults for old; (2) Schema Registry—versioned, backward/forward compatible; (3) Multiple readers—support versions during transition; (4) Dead-letter for incompatible. **Parquet/Delta**: mergeSchema=True. Avro:...
Red Flag: 'We'd restart the pipeline.' Pro-Move: 'Additive-only policy; new columns get default null; backfill job for historical.'
This easy-level System Design/Architecture question appears frequently in data engineering interviews at companies like Google. 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.
Strategy: (1) Schema evolution—additive changes; new columns, defaults for old; (2) Schema Registry—versioned, backward/forward compatible; (3) Multiple readers—support versions during transition; (4) Dead-letter for incompatible.
Parquet/Delta: mergeSchema=True. Avro: Schema Registry. Design for evolution; document breaking vs additive; compatibility tests in CI. Google: extensive versioning, canary deployments.
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Analyze My Answer — FreeAccording 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.