SPS-C01 Exam Dumps | You are designing a Snowpark application to process streaming data ingested into Snowflake using Snowpipe.

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Question 43/151

You are designing a Snowpark application to process streaming data ingested into Snowflake using Snowpipe. The application needs to apply a complex set of transformations and aggregations to the incoming data in real-time. Which of the following approaches would be MOST suitable for this scenario, leveraging the strengths of Snowpark architecture?

A. Continuously query the incoming data from Snowpipe using a Snowpark DataFrame and perform the transformations and aggregations on the client-side in a loop.

B. Use Snowpark to define a series of chained UDFs that perform the transformations and aggregations directly within the Snowpipe pipeline.

C. Create a Snowpark DataFrame that represents the incoming data and use the 'write_pandaS function to write the transformed data to a separate Snowflake table after each micro-batch.

D. Define a Snowpark Stored Procedure ('sproc') that is triggered automatically by a Snowflake Task whenever new data arrives via Snowpipe. The stored procedure performs the transformations and aggregations and stores the results in a new table.

E. Utilize Snowflake's Streams and Tasks feature and define views with complex SQL transformations that leverages Snowpipe.

Correct Answer: D

Using a Snowpark Stored Procedure triggered by a Snowflake Task provides the best solution for real-time processing of streaming data from Snowpipe. The Task automates the execution of the stored procedure whenever new data is available, and the stored procedure leverages Snowpark's server-side capabilities to perform the transformations and aggregations efficiently within the Snowflake environment. Option A involves client-side processing, Option B isn't compatible with Snowpipe directly and chained UDFs may not optimal for complex transformations and aggregations. Option C involves constant writes and may have performance issues with large datasets. While option E could work, utilizing Snowpark stored procedures provides better flexibility and Python code integration for more complex logic.

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