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What is the difference between Managed and External Tables in Databricks?

Spark/Big Dataeasy0.6 min read

**Managed tables**: Databricks/Spark owns both metadata and data; `DROP TABLE` deletes metadata and underlying data. **External tables**: Metadata is in the catalog; data lives in an external location (S3, ADLS, GCS); `DROP TABLE` removes only metadata; data persists. **Why it...

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Frequency

Low

Asked at 2 companies

Why This Question Matters

This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Altimetrik, Incedo. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (snowflake, spark) will help you answer variations of this question confidently.

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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

125 words

Managed tables: Databricks/Spark owns both metadata and data; DROP TABLE deletes metadata and underlying data. External tables: Metadata is in the catalog; data lives in an external location (S3, ADLS, GCS); DROP TABLE removes only metadata; data persists. Why it matters architecturally: Managed tables enforce a single lifecycle for schema and data. External tables decouple storage from compute, enabling multi-engine access (Snowflake, Athena, Redshift) and shared data lakes. Scalability trade-off: External tables scale storage and compute independently; managed tables bind them. Cost implication: External storage (S3/ADLS) is cheaper for cold data; managed tables can increase egress if data is co-located with compute. Example: CREATE EXTERNAL TABLE ... LOCATION 's3://bucket/path'. Use external for raw/curated layers, shared datasets, compliance retention; use managed for transient or ephemeral tables.

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 2 companies. 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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