What considerations should be taken into account when choosing between a dimensional model and a flattened data set for BI requirements in Snowflake? (Select all that apply)

Question

Options

• AFlexibility and ease of maintenance
• BAvailability of only specific query types
• CQuery complexity and performance expectations
• DData redundancy and normalization requirements

• A
  84% (52)
• B
  16% (10)

Explanation

When choosing between a dimensional model and a flattened dataset in Snowflake, A, C, and D are all valid engineering trade-offs that directly impact design decisions.

Why each correct answer applies:

• (A) Flexibility and ease of maintenance: Dimensional models (star/snowflake schemas) are easier to extend and modify as business requirements evolve, since changes are isolated to specific dimension or fact tables. Flattened datasets require broader restructuring when logic changes.

• (C) Query complexity and performance expectations: Flattened datasets reduce join overhead, making queries simpler and faster for straightforward BI tools. Dimensional models introduce multi-table joins but can be optimized for complex analytical patterns - so your expected query patterns drive the choice.

• (D) Data redundancy and normalization requirements: Flattened models denormalize data, trading storage efficiency for query simplicity. Dimensional models are more normalized, reducing redundancy but requiring joins. If data integrity and storage cost matter, normalization is a real factor.

Why B is wrong: Snowflake's SQL engine supports virtually all query types regardless of the modeling approach. The availability of query types is not constrained by your schema design - both models support aggregations, filters, window functions, etc.

Memory tip: Remember "FQD" - Flexibility, Query performance, Data normalization - the three real-world engineering levers. Option B is a red herring because Snowflake doesn't restrict what queries you can run based on schema shape.

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