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.

MediumTechnical

0 practiced

Your pipelines ingest event facts that sometimes arrive late by hours or days. How would you model your fact table and design ETL processes to support efficient re-processing and ensure correctness of historical aggregates without re-computing the entire dataset? Discuss partitioning, watermark strategies, idempotent ingestion, and techniques to minimize recompute.

MediumTechnical

0 practiced

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.

MediumTechnical

0 practiced

Compare schema-on-read (data lake) and schema-on-write (data warehouse) approaches from the perspective of data modeling and query performance. For analytical workloads that ingest semi-structured sources with frequent schema changes, explain when you'd choose schema-on-read versus schema-on-write and how each impacts model choices, query latency, and maintenance.

HardTechnical

0 practiced

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.

EasyTechnical

0 practiced

Explain materialized views (or materialized tables) and how they differ from standard views. Discuss refresh strategies (on-demand, scheduled, incremental), how they improve query performance for analytics, and downsides such as staleness, storage cost, and added complexity. Provide situations where materialized views are preferable to runtime aggregation.

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