I went straight to star schema and probably over-explained the basics before getting to the interesting stuff.
Start by clarifying the analytics requirements and data volume, then propose a dimensional model (star schema) with a central fact table and conformed dimensions. Walk through the grain of the fact table, dimension design (including slowly changing dimensions), and justify partitioning and storage choices based on query patterns and scale.
Pro tip: Emphasize that the schema should be driven by the business questions and query patterns, not by source system structure. Mention that at Apple's scale, partitioning and clustering are critical for performance and cost, and that you'd validate the design with real query workloads.
Ask about the analytics use cases, data volume, query patterns, and latency requirements. Identify key business processes and metrics to model.
Propose a star schema for simplicity and query performance, or a snowflake schema if dimension normalization is needed. Explain trade-offs and why star is often preferred for analytics.
Define the fact table grain (e.g., one row per transaction) and identify measures and foreign keys. Design dimensions with surrogate keys, denormalized attributes, and handle slowly changing dimensions (Type 2 for history).
Choose columnar storage (e.g., Parquet) for analytics. Partition by date or region to enable partition pruning, and cluster by high-cardinality columns used in filters. Discuss file sizes and compaction.
Test the schema against representative queries, measure performance, and adjust partitioning or indexing. Consider future scalability and evolving requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.