What are benefits of using Snowpark with Snowflake? (Choose two.)
What are benefits of using Snowpark with Snowflake? (Choose two.)
Snowpark does not require a separate cluster running outside of Snowflake; all computations are handled within Snowflake itself. Additionally, Snowpark executes as much work as possible within the source databases, including for operations involving User-Defined Functions (UDFs). These capabilities leverage Snowflake's powerful data processing and management capabilities, ensuring efficient and scalable performance.
There is no mention of D anywhere.. you need to migrate the job code
Option E :In general, Snow park will try to execute as much work as possible in the source databases, but there are some cases where it will need to transfer data to the server. The specific cases will depend on the operations that you are performing and the data that you are accessing. Let's say you want to join two tables in Snowflake. If the two tables are in the same database, then Snow park can execute the join operation in the source database. However, if the two tables are in different databases, then Snow park will need to transfer the data from one database to the other before it can execute the join operation.
https://docs.snowflake.com/en/developer-guide/snowpark/index Benefits When Compared with the Spark Connector In comparison to using the Snowflake Connector for Spark, developing with Snowpark includes the following benefits: Support for interacting with data within Snowflake using libraries and patterns purpose built for different languages without compromising on performance or functionality. Support for authoring Snowpark code using local tools such as Jupyter, VS Code, or IntelliJ. Support for pushdown for all operations, including Snowflake UDFs. This means Snowpark pushes down all data transformation and heavy lifting to the Snowflake data cloud, enabling you to efficiently work with data of any size. No requirement for a separate cluster outside of Snowflake for computations. All of the computations are done within Snowflake. Scale and compute management are handled by Snowflake.
https://www.snowflake.com/en/data-cloud/snowpark/spark-to-snowpark/ https://www.snowflake.com/en/data-cloud/snowpark/ https://docs.snowflake.com/en/developer-guide/snowpark/index https://medium.com/snowflake/pyspark-versus-snowpark-for-ml-in-terms-of-mindset-and-approach-8be4bdafa547#:~:text=Snowpark%20pushes%20all%20of%20its,leverage%20the%20power%20of%20Snowflake. https://www.snowflake.com/blog/snowpark-designing-performant-processing-python-java-scala/ https://docs.snowflake.com/en/user-guide/warehouses-snowpark-optimized
CE as per documentation. Pushdown means pushing as much as code to Source DB.
D is not an answer, document does not say this and snowpark has its own API which you need to use and cannot run spark code directly.. need to be customised
Correct