A company has an extensive script in Scala that transforms data by leveraging DataFrames. A Data Engineer needs to move these transformations to Snowpark.
What characteristics of data transformations in Snowpark should be considered to meet this requirement? (Choose two.)
When transitioning data transformations from Scala using DataFrames to Snowpark, it's crucial to note that Snowpark supports joining multiple tables using DataFrames, making it possible to execute complex data transformations. Additionally, Snowpark operations are executed lazily on the server. This means that transformations are not performed immediately but are deferred until an action is triggered, allowing for more efficient query execution.
Referring to Columns in Different DataFrames When referring to columns in two different DataFrame objects that have the same name (for example, joining the DataFrames on that column), you can use the DataFrame.col method in one DataFrame object to refer to a column in that object (for example, df1.col("name") and df2.col("name")). To retrieve and manipulate data, you use the DataFrame class. A DataFrame represents a relational dataset that is evaluated lazily: it only executes when a specific action is triggered. In a sense, a DataFrame is like a query that needs to be evaluated in order to retrieve data. https://docs.snowflake.com/en/developer-guide/snowpark/python/working-with-dataframes