Data Modeling for Query Performance Questions

Focuses on schema and data modeling choices that enable efficient querying at scale. Topics include normalization and denormalization trade offs, analytical schemas such as star schema and snowflake schema, the roles of fact tables and dimension tables, modeling for common query patterns and aggregations, and how model choices impact indexing, join costs, and storage. Candidates should be able to justify schema decisions based on query workload, discuss partitioning and sharding implications for model design, and propose modeling adjustments that improve query latency and maintainability.

EasyTechnical

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Discuss surrogate keys versus natural keys for dimension tables in an analytical warehouse. Cover their effects on join performance, storage size, ability to implement SCD (Type 2), referential stability when natural attributes change, and how surrogate keys influence ETL design and downstream consumers.

MediumTechnical

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Write a performant ANSI SQL query to compute per-user 7-day rolling revenue using window functions on a large events table. Explain how you would partition and order data physically (e.g., partition by user_id, cluster by event_date) so the query parallelizes well, and discuss distribution keys or sharding considerations if the table is distributed across nodes.

HardTechnical

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Design an incremental ETL process that maintains hourly and daily pre-aggregated summary tables from raw events with exactly-once semantics. Address idempotence, late-arriving events, CDC (change-data-capture) sources, checkpointing, state management, and how to re-compute only affected aggregates with minimal recomputation. Specify how you would test correctness.

HardTechnical

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Compare modeling and performance trade-offs between building traditional OLAP cubes (precomputed cubes/ROLAP/MOLAP) and using a serverless analytic engine (e.g., BigQuery) with denormalized tables and on-the-fly queries. Consider storage, precomputation time, query latency, freshness, operational complexity, and cost. When would you favor one approach over the other?

MediumTechnical

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You're building analytics for a multi-tenant SaaS platform. Compare three modeling strategies: single shared schema with tenant_id filters, per-tenant schema, and per-tenant database. Evaluate each for query performance, scalability, operational complexity, cost, and security. Recommend a strategy for a high-scale analytics workload and justify it.

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