**Architectural Logic**: Internal (native) tables: BigQuery owns metadata and storage (Colossus). Columnar layout, partitioning, clustering, automatic optimization. Dropping = data deleted. External tables: BigQuery owns metadata only; data lives in GCS, Drive, Bigtable....
Red Flag: Saying 'external is always cheaper'—storage vs compute trade-offs differ by workload. Pro-Move: 'We use external tables for raw GCS landing; internal for curated layers. Migration is a COPY job, not a rewrite.'
This hard-level SQL question appears frequently in data engineering interviews at companies like EY, Incedo, Tech Mahindra. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (bigquery, optimization, partition) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Architectural Logic: Internal (native) tables: BigQuery owns metadata and storage (Colossus). Columnar layout, partitioning, clustering, automatic optimization. Dropping = data deleted. External tables: BigQuery owns metadata only; data lives in GCS, Drive, Bigtable. Dropping = metadata deleted, data persists. Why: Internal enables DML, streaming inserts, partitioning strategies, cost-effective storage classes. External enables federated queries, zero-copy ingestion, multi-cloud patterns. Scalability: Internal scales with BigQuery's managed infra; external is bounded by external storage throughput and adds network hop. Cost: Internal storage ~$0.02/GB/month; external scans charge only compute (bytes read). For large ad-hoc scans on cold data, external can be cheaper if you read subsets. Use internal for production analytics, SCD, incremental pipelines; external for landing zones, federated access, regulatory "data-at-rest" requirements.
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 3 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.