Apache Spark 3 for Data Engineering and Analytics with Python - Working with User-Defined Functions

The video tutorial covers the creation and use of user defined functions (UDFs) in Spark. It begins with an introduction to the necessity of UDFs when Spark SQL functions are insufficient. The tutorial then demonstrates how to create a DataFrame with student names and scores, followed by writing a Python function to assign letter grades. Finally, the Python function is converted into a Spark UDF and applied to the DataFrame to calculate grades.

What is the primary limitation of using bare Python code for data transformations in Spark?

• It is too slow for large datasets.

• It requires additional libraries.

• It is not compatible with Spark SQL.

• It cannot be used to manipulate DataFrames.

Which function is imported to create user-defined functions in Spark?

• pandas_udf

• spark_udf

• UDF

• sql_udf

What is the purpose of the 'students_list' in the tutorial?

• To calculate the average score of students.

• To hold student names and their test scores.

• To display the list of students in alphabetical order.

• To store the names of the students only.

What is the initial value assigned to the 'grade' variable in the Python function?

• C

• Nothing

• B

• A

What is the purpose of converting the Python function into a Spark user-defined function?

• To enable the function to run on multiple nodes.

• To make the function compatible with other programming languages.

• To allow the function to be used within Spark DataFrames.

• To improve the performance of the function.

Which alias is used for the new column created by the user-defined function in the DataFrame?

• Result

• Grade

• Evaluation

• Score

What is the final output of applying the user-defined function to the DataFrame?

• A list of student names.

• A DataFrame with student names and their grades.

• A summary of test scores.

• A chart of student performance.