Which technique leverages performance efficiencies by enabling large and complex Spark logical plans to be processed in Snowflake, thus using Snowflake to do most of the actual work?

• A. Query Plan Pushdown
• B. Query Pushdown
• C. Query Optimization
• D. Pushdown is not possible in Spark UDFs

Answer: (B)

The technique is Query Pushdown. It works by offloading as much of the Spark plan as possible to Snowflake so Snowflake handles the heavy lifting—filters, projections, joins, and even some aggregations—while Spark just orchestrates and receives the results. This leverages Snowflake’s optimized execution engine, reduces data movement across the network, and lowers the workload on Spark workers, leading to faster overall performance for large and complex plans.

Other terms aren’t as precise for this scenario: “Query Plan Pushdown” isn’t the standard naming, “Query Optimization” is a broader process that can occur inside either engine and doesn’t specifically describe pushing work to Snowflake, and the notion that pushdown isn’t possible with Spark UDFs is not a defining characteristic of this technique.