Which of the following object types cannot be contained within a column of a Spark DataFrame?
Which of the following object types cannot be contained within a column of a Spark DataFrame?
Spark DataFrames are designed to store structured data where each column has a specific data type. While it can contain various data types such as strings, arrays, null values, and vectors, it cannot directly contain another DataFrame as a column. This is because a DataFrame is an abstraction for handling distributed data, and its columns are meant to hold individual data elements, not entire tables.
A. Spark DataFrames do not directly support containing other DataFrames as columns. A DataFrame column can only have one of the supported data types, such as primitive types (e.g., IntegerType, StringType, DoubleType, etc.) or complex types (e.g., ArrayType, MapType, StructType, etc.), but it cannot contain an entire DataFrame as a column.
Spark DataFrames are designed to store structured data, where each column has a specific data type. While DataFrames can contain various data types such as strings (option B), arrays (option C), null values (option D), and vectors (option E), they cannot directly contain other DataFrames (option A) as a column.