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.

EasyTechnical

69 practiced

Describe how columnar storage differs from row-oriented storage and why columnar formats such as Parquet or ORC are generally better suited for analytics workloads. Explain effects on I/O, compression, vectorized execution, and examples of query patterns (aggregates, selective scans) that benefit most.

HardSystem Design

73 practiced

You need sub-second dashboard responses for the top 10 metrics across 10M customers with 100M events/day. Design an architecture combining pre-aggregations, cache layers, materialized views, and a hot store to meet sub-second SLAs while balancing freshness and cost. Describe invalidation strategies and how to integrate with BI tools.

EasyTechnical

61 practiced

Define Change Data Capture (CDC). Describe a high-level architecture for using CDC to feed a cloud data warehouse to support near-real-time analytics. Include components such as source log capture, stream processing, staging, and merge into the warehouse and common pitfalls (schema drift, ordering).

HardSystem Design

67 practiced

A legacy Redshift cluster must migrate to Snowflake with minimal downtime and functionally equivalent queries. Outline a migration plan including schema conversion, data export/import (e.g., UNLOAD to S3 and COPY), handling Redshift-specific features (sort/dist keys), validation strategies, performance tuning steps post-migration, and estimated downtime/rehearsal approach.

MediumSystem Design

52 practiced

Outline an orchestration and monitoring strategy to keep materialized views or pre-aggregated tables fresh. Include how you'd detect staleness, schedule incremental refreshes, handle failures and retries, perform backfills, and ensure eventual consistency between base tables and aggregates.

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