**Why**: Invalid records corrupt downstream; validation at ingress isolates failures. **Components**: (1) Schema Registry (Avro/Proto/JSON Schema)—versioned schemas; (2) Validate at ingress—Kafka with Schema Registry, API gateway; (3) Check required fields, types, enums; (4)...
Pro-Move: 'We version schemas; invalid events go to DLQ with original payload + error—reprocess after schema fix.'
This easy-level System Design/Architecture question appears frequently in data engineering interviews at companies like Adidas. 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.
Why: Invalid records corrupt downstream; validation at ingress isolates failures.
Components: (1) Schema Registry (Avro/Proto/JSON Schema)—versioned schemas; (2) Validate at ingress—Kafka with Schema Registry, API gateway; (3) Check required fields, types, enums; (4) Dead-letter queue for invalid.
Evolution: Backward/forward compatible changes. Confluent Schema Registry; producers validate before produce. For JSON: jsonschema, pydantic. Quarantine invalid; alert on failure rate.
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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.