**Architectural Logic**: Metadata drives optimization and discovery; system catalogs are the source of truth. **Storage**: pg_catalog (PostgreSQL), information_schema (standard). Tables: pg_class (tables, indexes), pg_attribute (columns), pg_index. **Access**: `SELECT * FROM...
This hard-level SQL question appears frequently in data engineering interviews at companies like Tredence. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (bigquery, optimization, snowflake) 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: Metadata drives optimization and discovery; system catalogs are the source of truth. Storage: pg_catalog (PostgreSQL), information_schema (standard). Tables: pg_class (tables, indexes), pg_attribute (columns), pg_index. Access: SELECT * FROM information_schema.tables WHERE table_schema='public'. Why It Matters: Optimizer uses statistics for plans; tools use for discovery and lineage. Scalability: Maintain statistics (ANALYZE); extended properties for custom metadata. Warehouses: Snowflake (information_schema, account_usage); BigQuery (information_schema). Best practice: Use INFORMATION_SCHEMA for portability; document custom metadata.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL 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.