PySpark and AWS: Master Big Data with PySpark and AWS - Solution (UDFs)

The video tutorial guides viewers through reading data from a CSV file into a DataFrame, creating a new column for employee increments based on state-specific criteria, and writing a Python function to calculate these increments. The function is registered as a User Defined Function (UDF) and applied to the DataFrame. The tutorial also covers handling data types and debugging common errors.

What is the first step in processing the office data?

• Create a new column for increments

• Read data from a CSV file

• Write a function to calculate increments

• Register a UDF

Which state has an increment criteria of 10% of the salary?

• California

• Florida

• Texas

• New York

What is the increment percentage for employees in California?

• 5%

• 10%

• 3%

• 12%

How is the bonus calculated for employees in New York?

• 3% of the bonus

• 5% of the bonus

• 3% of the salary

• 5% of the salary

What is the purpose of registering a function as a UDF?

• To create a new DataFrame

• To calculate bonuses

• To use the function within a DataFrame operation

• To read data from a CSV file

What data type issue was encountered during the implementation?

• Boolean instead of string

• String instead of integer

• Float instead of integer

• Integer instead of float

What must be done after changing a cell in Databricks to apply the changes?

• Close and reopen the notebook

• Run the cell

• Save the notebook

• Restart the kernel