Apache Spark 3 for Data Engineering and Analytics with Python - Columns and Expressions

The video tutorial explores Spark functions for data frame operations, focusing on the call and expr functions. It demonstrates how to select columns using these functions and highlights their differences. The tutorial also covers concatenating names and calculating a salary increase using SQL functions in PySpark.

What is the primary focus of the initial section on Spark DataFrame operations?

• Exploring methods for handling columns and expressions

• Creating new DataFrames

• Learning about Spark streaming

• Understanding Spark architecture

Which function is used to select specific columns from a DataFrame?

• orderBy

• call

• groupBy

• filter

What is the main difference between 'call' and 'expr' functions?

• 'call' can perform explicit calculations

• 'call' is faster than 'expr'

• 'expr' can perform explicit calculations

• Both functions are identical

Which SQL function is used to concatenate first and last names?

• join

• merge

• concat

• concat_ws

How is a 10% salary increase calculated in the example?

• salary * 0.10

• salary + 0.10

• salary / 0.10

• salary * 1.10

What happens if you try to use a string expression with the 'call' function?

• It returns a null value

• It performs a different calculation

• It results in an error

• It works perfectly

What is the purpose of using an alias in the DataFrame operations?

• To rename a DataFrame

• To rename a column

• To change data types

• To filter data