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 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.

EasyTechnical

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Explain Slowly Changing Dimensions (SCD) Types 1, 2, and 3. For each type, describe how historical values are stored, query implications for analytics, and typical use cases. Give a short example of a schema change to implement SCD Type 2 for a customer dimension.

MediumTechnical

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Discuss how to choose optimal Parquet file sizes and the factors that influence that choice in cloud object storage. Explain how small files create overhead in query engines and how very large files affect parallelism and read latency. Recommend file size ranges and compaction strategies.

HardSystem Design

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Design an end-to-end cloud data warehouse architecture to support 50 PB of raw data and 100 TB of curated analytics tables, serving 1M analytical queries per day with mixed SLAs. Include choices for storage (object store vs managed storage), compute separation, partitioning/clustering strategy, caching, cost controls, and data movement patterns.

MediumTechnical

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Write a SQL MERGE statement (Snowflake or BigQuery syntax) that implements an SCD Type 2 upsert for a customer_dim table. Assume source staging table has customer_id, name, address, and effective_timestamp. The target customer_dim has surrogate_key, customer_id, name, address, valid_from, valid_to, is_current. Show how to close old records and insert new versions.

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