DEA-C02 Exam Dumps | You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data

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Question 11/149

You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data is structured as Avro records. You want to leverage Snowpipe for ingestion and Snowpark DataFrames for transformation. What is the MOST efficient and scalable approach to integrate these components?

A. Create a Kafka connector that directly writes Avro data to a Snowflake table. Then, use Snowpark DataFrames to read and transform the data from that table.

B. Use Snowpipe to ingest the Avro data to a raw table stored as binary. Then, use a Snowpark Python UDF with an Avro deserialization library to convert the binary data to a Snowpark DataFrame.

C. Configure Snowpipe to ingest the raw Avro data into a VARIANT column in a staging table. Utilize a Snowpark DataFrame with Snowflake's get_object field function on the variant to get an object by name, and create columns based on each field.

D. Convert Avro data to JSON using a Kafka Streams application before ingestion. Use Snowpipe to ingest the JSON data to a VARIANT column and then process it using Snowpark DataFrames.

E. Create external functions to pull the Avro data into a Snowflake stage and then read the data with Snowpark DataFrames for transformation.

Correct Answer: D

Option D is generally the most efficient. Converting Avro to JSON before ingestion simplifies the integration with Snowpipe and Snowpark. Snowpipe is optimized for semi-structured data like JSON within a VARIANT column. Subsequently, Snowpark DataFrames can easily process the JSON data using built-in functions, avoiding the complexity and potential performance bottlenecks of UDFs (Option B) or custom connectors (Option A). Although Snowflake's function can work with variant data, operating on raw Avro data is not natively supported by Snowpipe without pre-processing or complex UDF logic. External functions (Option E) add another layer of complexity for data retrieval.

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