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.

MediumTechnical

0 practiced

You observe a slow join between a 200M-row fact table and a 10M-row dimension table in your cloud warehouse. Describe practical optimization steps to reduce query time: consider sort/distribution keys, broadcasting vs shuffling, pre-aggregation, materialized views, and statistics. Give concrete commands or SQL where appropriate.

HardSystem Design

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Design a metadata and lineage system for a cloud data warehouse that supports governance, impact analysis, and reproducibility at scale. Describe what metadata to capture (schema, table stats, job runs, data owners), how to collect it, and how to expose lineage to analysts and auditors.

MediumSystem Design

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You store daily Parquet files partitioned by dt in S3 and query them with Athena/Glue. Describe an optimal partitioning and file-sizing strategy to minimize query latency and cost. Discuss use of partition pruning, Glue catalog partitions, and cost of too many small files versus too-large files.

MediumTechnical

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Compare using materialized views (or search-optimized precomputed tables) versus creating scheduled pre-aggregation ETL jobs. When would you use materialized views provided by the cloud warehouse product, and when are bespoke pre-aggregation tables preferable?

MediumTechnical

0 practiced

Explain how table statistics (histograms, column cardinality, row counts) affect query planning in cloud data warehouses. Describe commands or processes (ANALYZE, COMPUTE STATISTICS) on platforms like Redshift, Snowflake, or BigQuery and why up-to-date stats are important.

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