DEA-C02 Exam Dumps | A Snowflake data pipeline utilizes Snowpipe to ingest JSON data from cloud storage into a raw staging

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Question 55/149

A Snowflake data pipeline utilizes Snowpipe to ingest JSON data from cloud storage into a raw staging table 'RAW DATA' Subsequently, a series of transformation tasks are executed to cleanse, transform, and load the data into fact and dimension tables. You've noticed significant performance degradation in the transformation tasks, especially when dealing with large JSON payloads and deeply nested structures. Which of the following optimization techniques, applied at different stages of the pipeline, would MOST likely improve the overall performance of the data transformation tasks?

A. Increasing the virtual warehouse size used by the transformation tasks to provide more compute resources.

B. Using the file format option when defining the Snowpipe integration to remove the outer array from the JSON data before ingestion.

C. Employing Snowflake's 'LATERAL FLATTEN' function with appropriate 'PATH' expressions to efficiently extract the required attributes from the JSON data during transformation.

D. Partitioning the 'RAW DATA' staging table based on the ingestion timestamp to reduce the amount of data scanned during transformation.

E. Replacing the transformation tasks with external functions implemented in Python using Snowpark, leveraging the power of Pandas DataFrames for JSON processing.

Correct Answer: A,C,D

Options A, C, and D address different aspects of performance optimization. Increasing the virtual warehouse size (A) provides more resources for the transformation tasks. Using SLATERAL FLATTEN' effectively (C) optimizes JSON parsing. Partitioning the ' RAW_DATR table (D) reduces the data scanned, improving query performance. While (B) can be helpful, it depends on the JSON structure. Snowpark UDFs (E) can introduce overhead due to the serialization and deserialization between Snowflake and the external environment.

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