Cloud Data Warehouse Design and Optimization Questions

Covers design and optimization of analytical systems and data warehouses on cloud platforms. Topics include schema design patterns for analytics such as star schema and snowflake schema, purposeful denormalization for query performance, column oriented storage characteristics, distribution and sort key selection, partitioning and clustering strategies, incremental loading patterns, handling slowly changing dimensions, time series data modeling, cost and performance trade offs in cloud managed warehouses, and platform specific features that affect query performance and storage layout. Candidates should be able to discuss end to end design considerations for large scale analytic workloads and trade offs between latency, cost, and maintainability.

HardSystem Design

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Design an efficient cold/hot data tiering strategy in a cloud warehouse that minimizes cost while keeping active queries fast. Include policies for moving partitions between tiers, query routing or unified views to access both tiers transparently, lifecycle automation, and how to preserve query correctness and acceptable latency.

EasyTechnical

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As a BI analyst designing warehouse retention policies, explain how GDPR and the 'right to be forgotten' impact data warehouse schema, retention, and auditability. Propose practical approaches to implement deletions or anonymization while preserving analytics capabilities and audit trails.

MediumTechnical

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How would you implement row-level security (RLS) for multi-tenant dashboards in a cloud warehouse (Snowflake or BigQuery)? Discuss policy design, performance implications, alternatives such as separate datasets, and how to maintain and audit RLS rules at scale.

MediumSystem Design

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Design partitioning and clustering in BigQuery for telemetry ingesting ~1 TB/day to support hourly and daily aggregations. Specify partition field and granularity, clustering columns, compaction/ingestion strategy to reduce small files, and how to handle retention for long-term cost savings.

EasyTechnical

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Explain Slowly Changing Dimensions (SCD) types 0, 1, 2, and 3. For a customer dimension where addresses change frequently and history is needed for audits, which SCD type would you choose and why? Discuss how the choice affects storage, query complexity, and ETL design.

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