COF-C03 Question 124
Single answerSnowparkA data engineering team uses Snowpark for Python to transform a large SALES table and produce a curated RESULT table each night. The current Python job reads data into a local pandas DataFrame, applies filtering and column calculations, and then writes the results back to Snowflake. The job is slow and frequently fails when data volume spikes. The team wants to redesign the process to better align with Snowpark best practices while minimizing data movement and improving scalability. Which approach should they take?
- A
Use Snowpark DataFrames to express the filtering and column transformations so the processing runs in Snowflake, and write the final result back to a Snowflake table.
- B
Export the SALES table from Snowflake to an external stage first, process the files in Python outside Snowflake, and reload the transformed files into the RESULT table.
- C
Convert the SALES table to a materialized view before loading it into pandas so Snowpark can optimize the local Python transformations.
- D
Use Snowpark to retrieve all rows into the client application with collect(), perform the transformations in Python memory, and then save the transformed data back to Snowflake.
Show answer and explanation
Correct answer: A
Explanation
Snowpark is designed so developers can use familiar languages such as Python to define data transformations while Snowflake executes those operations in its own engine. This pushdown model is a core advantage of Snowpark: data remains in Snowflake, reducing network transfer and client memory constraints. In this scenario, the existing bottleneck comes from pulling a large dataset into pandas on the client. Best practice is to use Snowpark DataFrame operations for filtering, projections, and derived columns, then persist the result back to a Snowflake table. Methods like collect() should generally be reserved for small result sets or inspection, not bulk ETL. This aligns with Snowflake documentation and Snowpark guidance emphasizing in-platform processing, reduced data movement, and scalable execution using Snowflake compute.
- A. Correct.
Correct. Snowpark DataFrames allow developers to define transformations in Python while the execution occurs in Snowflake. This minimizes data movement, leverages Snowflake compute for scalability, and is the recommended pattern for large-scale data transformation. Writing the final output back to a Snowflake table keeps processing close to the data and avoids client-side memory bottlenecks.
- B. Incorrect.
Incorrect. Exporting data out of Snowflake for external processing increases data movement, operational complexity, and latency. This approach works against the stated goal of minimizing data movement and does not reflect typical Snowpark best practices when the transformations can be expressed and executed inside Snowflake.
- C. Incorrect.
Incorrect. Materialized views can improve performance for certain repeated query patterns, but they do not make pandas-based client-side transformations execute inside Snowflake. Snowpark does not optimize local pandas processing simply because the source is a materialized view. The main issue here is unnecessary data movement to the client.
- D. Incorrect.
Incorrect. Calling collect() retrieves the data from Snowflake to the client, which is precisely what causes scalability and memory problems for large datasets. While collect() is useful for small result sets, it is not appropriate for nightly large-scale transformations that should be pushed down to Snowflake.