Data Warehouse and Dimensional Modeling Questions

Design and model scalable analytical data systems using dimensional modeling principles and data warehouse architecture patterns. Core concepts include fact and dimension tables, defining and enforcing grain, surrogate keys, degenerate and role playing dimensions, conformed dimensions, and handling slowly changing dimensions including Type One, Type Two, and Type Three. Understand schema choices and trade offs such as star schema versus snowflake schema, normalization versus denormalization, and fact table types including transactional, periodic snapshot, and accumulating snapshot. Apply design decisions to meet query patterns and performance goals by considering partitioning, indexing, compression, columnar storage, and aggregation strategies. Be able to design schemas for different business domains, reason about data integration and consistency, and optimize for common analytical workloads and reporting requirements.

EasyTechnical

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Explain surrogate keys versus natural keys in dimension tables. Discuss pros and cons of surrogate keys, how they enable Type 2 Slowly Changing Dimensions, and practical approaches to generate surrogate keys both in batch and streaming ETL pipelines while avoiding collisions.

HardTechnical

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You need to add a new attribute to a dimension and change a column's data type that is used in production ML features and nightly training jobs. Outline a schema migration plan that ensures backward compatibility, avoids breaking feature serving and training, includes schema versioning and a rollout plan (canary, validation), and a rollback strategy.

HardTechnical

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In distributed query engines (Spark, Presto), explain criteria for choosing broadcast (map-side) joins versus shuffle (sort-merge) joins when joining large fact tables to dimension tables. Discuss thresholds for broadcast, memory requirements, handling skew, and query rewrites or hints you might use to force a particular join strategy.

HardSystem Design

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Design an architecture to introduce Change Data Capture (CDC) into an existing warehouse to support near-real-time updates and SCD Type 2 history. Include source connectors, message ordering and guarantees, idempotent writes, consumer-side processing, and how to handle network failures and event replay. Explain the pitfalls of schema changes and how to handle them safely.

EasyTechnical

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Explain the concept of 'grain' when designing a fact table. Provide at least three examples of poorly chosen grain and the problems those choices cause (e.g., aggregation errors, duplicate counting). Describe how you would document and enforce grain in ETL pipelines so downstream analysts don't misinterpret measures.

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