ARA-C01 Question 331
Single answerPythonA data platform team is standardizing how Python code is executed in Snowflake. They have two requirements for a new enrichment process: (1) the logic must run close to the data without moving large tables out of Snowflake, and (2) the process must apply complex Python transformations across millions of rows and write the results back to Snowflake tables on a scheduled basis. A solution architect wants to minimize data movement and avoid row-by-row execution bottlenecks. Which approach best meets these requirements?
- A
Implement the logic as a Python UDF and call it for each row in a SQL UPDATE statement.
- B
Implement the logic as a Python stored procedure using Snowpark DataFrames to process the data in Snowflake, then schedule it with a task.
- C
Extract the source tables to an external Python application, transform the data with pandas, and reload the results into Snowflake each time the schedule runs.
- D
Use a Python external function so the transformation runs in an external service while Snowflake sends rows to it during query execution.
Show answer and explanation
Correct answer: B
Explanation
For large-scale Python-based data transformations in Snowflake, the recommended architectural pattern is to use Snowpark for Python within a Python stored procedure when the process needs to read Snowflake data, apply transformation logic at scale, and write results back to Snowflake objects. This approach executes close to the data and avoids exporting large tables to external systems. It also supports orchestration of SQL and DataFrame operations in one server-side unit. To automate execution, Snowflake tasks can schedule the stored procedure.
Why not the other options? Python UDFs are valuable for reusable function logic inside SQL expressions, but they are not the ideal primary mechanism for end-to-end bulk transformation pipelines, especially when procedural control flow and writes to target tables are required. External applications and external functions both increase data movement or external dependencies, which conflicts with the stated requirement.
This aligns with Snowflake best practices around minimizing data movement, using Snowpark for in-platform processing, and using tasks for scheduled execution. Relevant Snowflake documentation includes the sections on Snowpark for Python, Python stored procedures, user-defined functions, external functions, and tasks.
- A. Incorrect.
This is not the best choice for the scenario. Python UDFs are useful for encapsulating scalar or vectorized logic that can be invoked from SQL, but using a Python UDF in a row-by-row SQL UPDATE pattern can introduce performance and operational limitations for large-scale table transformations. The requirement emphasizes processing millions of rows efficiently and writing results back on a schedule. A Python stored procedure with Snowpark is better suited for orchestrating set-based transformations and DML against Snowflake tables.
- B. Correct.
This is the best answer. Python stored procedures support procedural orchestration inside Snowflake, and Snowpark for Python allows developers to express transformations with DataFrames that execute in Snowflake. This keeps compute close to the data, minimizes movement, and avoids an inefficient row-by-row pattern. The stored procedure can read from Snowflake tables, transform data using Snowpark, and write results back to target tables. A task can then schedule the procedure to run automatically, which directly satisfies the scheduled execution requirement.
- C. Incorrect.
This does not meet the architecture goals well. Although an external Python application with pandas can perform complex transformations, it requires moving large datasets out of Snowflake and then reloading them. That increases data movement, operational complexity, latency, and potentially cost. It also contradicts the explicit requirement to run close to the data.
- D. Incorrect.
This is a plausible distractor because it uses Python-related integration, but it is not the best fit. External functions call out to remote services, typically through API integration, and are intended for invoking external processing from SQL. They introduce data egress and network dependency, and they are not the preferred pattern for large internal table-to-table transformations that can be handled directly in Snowflake. They also do not inherently provide the best mechanism for bulk write-back orchestration compared with Python stored procedures plus tasks.