What Snowflake features should be leveraged when modeling using Data Vault?

Question

Options

• ASnowflake's support of multi-table inserts into the data model's Data Vault tables
• BData needs to be pre-partitioned to obtain a superior data access performance
• CScaling up the virtual warehouses will support parallel processing of new source loads
• DSnowflake's ability to hash keys so that hash key joins can run faster than integer joins

• A
  84% (32)
• B
  8% (3)
• C
  3% (1)
• D
  5% (2)

Explanation

Data Vault modeling involves loading data into multiple target tables simultaneously - Hubs, Links, and Satellites are often populated from the same source extract. Snowflake's support for multi-table inserts (A) maps perfectly to this pattern, enabling a single source dataset to fan out into multiple Data Vault entities in one pass, reducing redundant reads and simplifying ELT pipelines. Pre-partitioning data (B) is an anti-pattern in Snowflake - Snowflake's automatic micro-partition pruning eliminates the need for manual partitioning, and attempting it would add unnecessary complexity. Scaling up warehouse size (C) adds per-query compute power but does not inherently support the parallel multi-source loading pattern that defines Data Vault. Snowflake does not hash join keys for performance (D) - Data Vault uses hash keys for surrogate key generation and portability, but Snowflake's query optimizer does not use hash-based lookups in the way described; integer and hash key joins perform comparably.

No community discussion yet for this question.