**Architectural Logic**: Normalize for writes; denormalize for analytics. **OLTP**: users (user_id PK, email, created_at); artists (artist_id PK, name); tracks (track_id PK, artist_id FK, title, duration); playlists (playlist_id PK, user_id FK, name); playlist_tracks...
This medium-level SQL question appears frequently in data engineering interviews at companies like media.net. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
Architectural Logic: Normalize for writes; denormalize for analytics. OLTP: users (user_id PK, email, created_at); artists (artist_id PK, name); tracks (track_id PK, artist_id FK, title, duration); playlists (playlist_id PK, user_id FK, name); playlist_tracks (playlist_id, track_id, position). Analytics: fact_listening (user_id, track_id, played_at, duration_played)—partition by played_at. dim_user, dim_artist, dim_track, dim_date. Why Partition: Time-series queries on listening history; conformed dimensions for cross-mart analysis. Scalability: Support many-to-many (collabs) via track_artists; surrogate keys for SCD. Cost: Separate hot (recent listening) from cold (historical) via partitioning.
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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.