SPS-C01 Exam Dumps | You are developing a Snowpark Python application to process streaming data from a Kafka topic, enrich

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Question 25/151

You are developing a Snowpark Python application to process streaming data from a Kafka topic, enrich it with data from a Snowflake table, and store the results in another Snowflake table. The enrichment process involves joining the streaming data with a large dimension table in Snowflake. Which of the following Snowpark features would be most efficient and scalable for this use case, considering the continuous nature of the streaming data and the size of the dimension table?

A. Using a standard Snowpark DataFrame join operation with a broadcast hint on the smaller streaming data DataFrame.

B. Utilizing Snowpark UDFs to fetch data from the dimension table for each row of the streaming data DataFrame.

C. Employing a Snowpark Stored Procedure that periodically refreshes a smaller, aggregated version of the dimension table and using that for joining with the streaming data.

D. Creating a Snowflake materialized view that pre-joins the streaming data (landing in a table) with the dimension table and using Snowpark to query the materialized view.

E. Leveraging a dynamic table to incrementally update the join of the streaming data and dimension table.

Correct Answer: E

Dynamic tables are designed for incremental data transformations, which is ideal for continuous streaming data processing and joining with a large dimension table. They automatically manage data refreshes and optimize performance for incremental updates, making them the most efficient and scalable option. Option A might work for small datasets, but it doesn't scale well with larger dimension tables or sustained streaming. Option B is generally inefficient for large-scale joins. Option C adds unnecessary complexity and latency due to the periodic refresh. Option D is not well-suited for true streaming as it relies on landing data into a static table first.

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