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

Describe how you would implement SCD Type 2 for a customer dimension containing 100M rows with frequent updates. Focus on storage layout (partitioning/clustering keys), surrogate keys, merge/upsert strategy, and query patterns for retrieving current vs historical records efficiently at scale.

HardTechnical

0 practiced

Design a bitemporal SCD Type 2 schema to support back-dated corrections and point-in-time analysis for legal/regulatory reporting. Include table structures with business_time (valid_from/valid_to) and system_time (sys_from/sys_to), primary keys, indexing/clustering, and how queries are written to retrieve the record state at an arbitrary business time. Discuss storage cost and query performance implications.

MediumTechnical

0 practiced

Explain partition pruning and design a partitioning scheme in BigQuery or Snowflake for a fact table that is queried mostly by event_date and device_type. Specify partition field/granularity, clustering columns (if applicable), and how pruning reduces bytes scanned and cost.

MediumSystem Design

0 practiced

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.

MediumTechnical

0 practiced

Describe how you would benchmark and estimate monthly costs for a given analytics workload on Snowflake and BigQuery. What metrics and traces would you collect (e.g., bytes scanned, compute seconds, concurrency), how would you simulate concurrent users, and how to extrapolate to monthly billing including storage growth?

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