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

81 practiced

What is a surrogate key in a dimensional model and why is it used instead of relying solely on natural/source keys? Describe advantages (e.g., performance, stable joins, SCD management) and potential pitfalls (re-keying, mapping tables). Outline a simple ETL approach to generate surrogate keys when ingesting data from multiple sources.

HardTechnical

88 practiced

Design an accumulating snapshot fact to track the order-to-cash funnel with milestones: order_placed, payment_received, shipped, delivered, refunded. Define the grain, columns (milestone timestamps, duration measures), dimensions to include, how to update milestone timestamps (idempotent updates), how to handle multiple shipments/refunds per order, and how to compute time-to-event metrics efficiently.

MediumTechnical

83 practiced

You are modeling analytics for an online learning platform. You have events: course_enrollment (one-per-enrollment), daily_progress (percentage completed per user per day), and course_lifecycle (multiple milestones per course from enrollment to certification). For the following reporting needs specify which fact-table type is appropriate and justify: 1) Track each enrollment event. 2) Measure daily progress and retention. 3) Measure time between enrollment and certification for each learner.

EasyTechnical

78 practiced

List essential columns for a Date dimension used in analytics and reporting. Include keys and common useful attributes such as fiscal flags, rolling period indicators, and marketing-friendly fields. Explain differences when modeling fiscal calendars vs calendar calendars and what extra attributes a finance team might require.

MediumTechnical

88 practiced

Explain how columnar storage and compression techniques (e.g., run-length encoding, dictionary encoding, delta encoding) benefit analytical queries. Discuss how attribute cardinality (low vs high) affects compressibility, dictionary size, and query performance, and what that implies for selecting storage formats or column encodings in a warehouse.

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