A JSON file, that contains lots of dates and arrays, needs to be processed in Snowflake. The user wants to ensure optimal performance while querying the data. How can this be achieved?

Question

Options

• AFlatten the data and store it in structured data types in a flattened table. Query the table.
• BStore the data in a table with a variant data type. Query the table.
• CStore the data in a table with a vai : ant data type and include STRIP_NULL_VALUES while
• DStore the data in an external stage and create views on top of it. Query the views.

• A
  4% (1)
• B
  91% (21)
• C
  4% (1)

Explanation

Storing JSON data in a VARIANT column is Snowflake's recommended and optimized approach for semi-structured data. Snowflake automatically parses, columnarizes, and compresses VARIANT data at load time, enabling efficient querying using dot notation and lateral flattening. The engine's automatic schema detection and columnar micro-partition storage provide strong query performance without requiring manual schema changes. Flattening into a structured table (A) can improve performance in some cases but adds ETL complexity and loses flexibility. Stripping null values (C) is a load option that reduces storage slightly but doesn't inherently improve query performance. Querying from an external stage via views (D) bypasses Snowflake's optimized storage and is generally the slowest option.

No community discussion yet for this question.