ARA-C01 Question 333
Single answerScalaA data engineering team is building a Snowpark application in Scala to standardize customer data before loading it into curated tables. The logic includes parsing nested JSON, applying reusable transformations, and joining to reference data already stored in Snowflake. The architects want most processing to run inside Snowflake for scalability and to minimize data movement. Which approach best meets these requirements?
- A
Implement the transformations with Snowpark for Scala DataFrame APIs so the logic is compiled into SQL and executed in Snowflake, using joins against Snowflake tables directly.
- B
Export the source data from Snowflake to an external Spark cluster, perform all Scala transformations there, and write the results back to Snowflake to reduce warehouse usage.
- C
Use Scala only to orchestrate row-by-row processing in the client application, fetching the data locally with JDBC ResultSets and applying transformations before reinserting records.
- D
Create a Java UDF for every transformation step and chain them together from Scala, because Snowpark for Scala cannot express joins or semi-structured data processing.
Show answer and explanation
Correct answer: A
Explanation
Snowpark for Scala enables developers to use Scala to build DataFrame-based transformations that Snowflake optimizes and executes within the platform. For architect-level decision making, the key principle is to keep data processing close to the data whenever possible. In this scenario, Snowpark for Scala is the best choice because it supports reusable transformation logic, joins against existing Snowflake tables, and operations on semi-structured data without exporting data to external systems. Using JDBC row-by-row processing or offloading all logic to Spark introduces avoidable latency, governance challenges, and operational burden. While UDFs have a place for encapsulating specific custom functions, they are not a substitute for a complete transformation pipeline when Snowpark APIs can model the logic directly. This aligns with Snowflake best practices around minimizing data movement and leveraging Snowpark for in-platform processing.
- A. Correct.
Correct. Snowpark for Scala is designed for this use case: developers write transformations in Scala using the Snowpark DataFrame API, and Snowflake pushes execution down into the Snowflake engine. This minimizes data movement, allows joins with Snowflake-resident reference tables, and supports working with semi-structured data such as JSON using Snowflake-native constructs. This is the preferred architectural pattern when the goal is to keep computation inside Snowflake.
- B. Incorrect.
Incorrect. Although Spark can process Scala workloads, exporting data out of Snowflake introduces unnecessary data movement, additional infrastructure, and operational complexity. It also contradicts the requirement to keep most processing inside Snowflake. An external Spark cluster may be valid for certain specialized use cases, but it is not the best fit here.
- C. Incorrect.
Incorrect. Fetching data to the client and processing row by row through JDBC is inefficient, does not scale well, and bypasses Snowflake's set-based execution model. This approach increases network transfer, reduces performance, and undermines the requirement to run processing inside Snowflake.
- D. Incorrect.
Incorrect. Java or Scala UDFs can be useful for specific custom logic, but they are not the primary mechanism for building an end-to-end transformation pipeline when Snowpark DataFrames can express the workflow directly. Snowpark for Scala supports joins and can work with semi-structured data, so creating a separate UDF for every step would add unnecessary complexity and overhead.