COF-C03 Question 125
Single answerSnowparkA data engineering team is building a customer churn feature pipeline in Snowflake using Snowpark for Python. The source tables are already in Snowflake, and the team wants to transform and join large datasets without moving data out of Snowflake. They also want to avoid row-by-row processing in the client application because performance has been poor in earlier prototypes. Which approach should the team use?
- A
Use Snowpark DataFrame transformations and actions so the logic is translated into SQL and executed in Snowflake
- B
Convert the Snowflake tables to pandas DataFrames in the client, perform all joins and aggregations locally, and then write the result back to Snowflake
- C
Export the source tables to cloud storage, process them in an external Spark cluster, and reload the transformed results into Snowflake
- D
Use Snowpark only to fetch rows one at a time from Snowflake and apply Python loops for the transformations
Show answer and explanation
Correct answer: A
Explanation
Snowpark is designed to let developers build data pipelines using familiar programming languages while executing the work inside Snowflake. For SnowPro Core, the key concept is that Snowpark DataFrame transformations are lazy and are pushed down to Snowflake for execution, rather than processing data row by row in the client. This reduces data movement, improves scalability, and takes advantage of Snowflake's compute engine. Best practice is to use Snowpark DataFrame operations for joins, filters, aggregations, and other transformations when the data is already stored in Snowflake. This aligns with Snowflake documentation describing Snowpark as a developer framework for processing data in Snowflake using DataFrames and user-defined logic executed in Snowflake.
- A. Correct.
Correct. Snowpark lets developers use DataFrame-style APIs in languages such as Python, Java, and Scala while keeping computation in Snowflake. Transformations are lazy and are compiled into SQL for execution by Snowflake. This is the preferred approach when the data already resides in Snowflake and the goal is to minimize data movement and leverage Snowflake compute.
- B. Incorrect.
Incorrect. Pulling large datasets into pandas moves processing out of Snowflake and is typically less scalable for large joins and aggregations. While pandas can be useful for small local analysis tasks, it does not align with the scenario's requirement to avoid moving data and to improve performance on large datasets.
- C. Incorrect.
Incorrect. Although external processing platforms can be appropriate in some architectures, this option adds unnecessary data movement and operational complexity when the data is already in Snowflake and Snowpark can perform the transformations directly in-platform.
- D. Incorrect.
Incorrect. Fetching rows one by one and processing them in Python loops is a common anti-pattern for analytical workloads. It bypasses Snowflake's set-based processing strengths and usually performs worse than using Snowpark DataFrame operations that execute inside Snowflake.