Managed: Spark/Hive owns metadata and data. DROP TABLE deletes both. External: Metadata only; data lives in specified location. DROP TABLE drops metadata; data remains. Why External: Shared data across tools (Athena, Glue, Spark); production datasets where accidental DROP would...
Red Flag: Only defining the difference without discussing data ownership or drop behavior. Pro-Move: 'All prod tables are External; we had an incident where Managed DROP cascaded to S3—now we never use Managed for shared data'—demonstrates operational lessons.
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Citi, Dunnhumby, Fragma Data Systems. 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.
Managed: Spark/Hive owns metadata and data. DROP TABLE deletes both. External: Metadata only; data lives in specified location. DROP TABLE drops metadata; data remains. Why External: Shared data across tools (Athena, Glue, Spark); production datasets where accidental DROP would be catastrophic; data lifecycle independent of table. Why Managed: Ephemeral tables, temp outputs; simpler—no orphan paths. Cost: Managed DROP can trigger expensive recursive deletes on object store. External DROP is cheap. Best practice: External for production; Managed for staging. Document ownership of underlying paths.
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