Start by clarifying the scope and scale (e.g., Netflix's ads tier), then walk through the core operational entities and their relationships, followed by the analytical star schema. Emphasize how the model supports key business processes like billing, attribution, pacing, and frequency capping, and discuss trade-offs between normalization and denormalization.
Pro tip: Demonstrate awareness of Netflix's unique context: ads are a secondary revenue stream and must not degrade the viewing experience, so frequency capping and pacing are critical. Also, mention that the data model should support both real-time and batch processing for different use cases.
Ask about scale, latency requirements, and specific Netflix constraints (e.g., ad load, user experience). Confirm whether the focus is on OLTP, OLAP, or both.
Define entities like Advertiser, Campaign, AdGroup, Creative, Targeting, Budget, and Bid, with their attributes and relationships. Use normalization to avoid redundancy.
Create fact tables (e.g., Impressions, Clicks, Conversions) and dimension tables (e.g., Time, User, Creative, Campaign) for reporting and analytics. Consider slowly changing dimensions.
Explain how the model enables billing (e.g., based on impressions/clicks), attribution (e.g., last-click, multi-touch), pacing (budget and bid adjustments), and frequency capping (user-level ad exposure tracking).
Address trade-offs between normalization and performance, and how to scale for Netflix's global user base. Mention partitioning, indexing, and potential use of NoSQL for certain workloads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.