Discover how Snowflake’s multi-table inserts streamline Data Vault modeling, ensuring data consistency across Hub, Link, and Satellite tables. Learn why Snowflake’s automatic optimizations eliminate the need for manual partitioning and hashing, while scaling virtual warehouses enables efficient parallel processing.
Table of Contents
Question
What Snowflake features should be leveraged when modeling using Data Vault?
A. Snowflake’s support of multi-table inserts into the data model’s Data Vault tables
B. Data needs to be pre-partitioned to obtain a superior data access performance
C. Scaling up the virtual warehouses will support parallel processing of new source loads
D. Snowflake’s ability to hash keys so that hash key joins can run faster than integer joins
Answer
A. Snowflake’s support of multi-table inserts into the data model’s Data Vault tables
Explanation
A. Snowflake’s support of multi-table inserts into the data model’s Data Vault tables. Data Vault modeling in Snowflake benefits from multi-table inserts, which allow loading data into multiple tables simultaneously within a single transaction.
This ensures data consistency across the Hub, Link, and Satellite tables. Snowflake’s automatic partition pruning optimizes query performance without the need for manual pre-partitioning. Scaling up virtual warehouses enables parallel processing, but is not specific to Data Vault. Hashing keys is not necessary as Snowflake efficiently handles joins on integer keys.
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