Start by clarifying the business questions and query patterns the model must support, then design a star schema with conformed dimensions and fact tables at the appropriate grain. Explain your grain choices, SCD strategies, partitioning, and how the model optimizes for common analytics like DAU, retention, and engagement.
Pro tip: Mention that Discord's scale demands partitioning by date and possibly server_id, and that using a Type 2 SCD for users and servers is critical for accurate historical analysis, but balance storage costs with query performance.
Ask about the key metrics (e.g., daily active users, messages per server, retention) and query patterns (e.g., time-series analysis, cohort analysis) to ensure the model serves its purpose.
Define dimensions (e.g., dim_user, dim_server, dim_date) and fact tables (e.g., fact_server_activity, fact_message_activity) based on the raw events, ensuring they capture the necessary measures and foreign keys.
For each fact table, decide the grain (e.g., one row per server per day for activity, one row per message for message events) and justify why it supports the required queries efficiently.
Explain how to track historical changes for users (e.g., username, avatar) and servers (e.g., name, owner) using Type 2 SCDs with effective dates and current flags, and discuss trade-offs.
Describe partitioning strategies (e.g., by date on fact tables, by server_id for large dimensions) and how the model optimizes for common queries like aggregations over time and joins.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.