Apache Spark 3 for Data Engineering and Analytics with Python - Distinct and Filter Transformations

The video tutorial covers RDD transformations in Spark, focusing on the distinct and filter functions. It explains how to remove duplicates using the distinct function and highlights the immutability of RDDs, emphasizing the need to create new RDDs for transformations. The tutorial also demonstrates the use of the filter transformation with a custom function to filter words starting with a specific letter. The importance of using Lambda functions in filtering is discussed, and the tutorial concludes with a summary of key points.

What is the primary purpose of the distinct transformation in RDDs?

• To filter records based on a condition

• To remove duplicate records

• To sort the data

• To count the number of records

How many records were initially counted in the RDD before applying the distinct transformation?

• 15

• 16

• 18

• 17

What is a key characteristic of RDDs that affects how transformations are applied?

• They automatically update

• They are immutable

• They are stored in a database

• They are mutable

What must be done to effectively apply a transformation to an RDD?

• Delete the original RDD

• Print the transformation result

• Assign the transformation result to a new RDD

• Save the transformation to a file

What is the purpose of the filter transformation in RDDs?

• To remove duplicates

• To sort the data

• To select records based on a condition

• To merge two RDDs

Which Python feature is commonly used with the filter transformation to evaluate conditions?

• Modules

• Decorators

• Classes

• Lambda functions

What does the lambda function do in the context of the filter transformation?

• It duplicates the records

• It evaluates each record against a condition

• It sorts the data

• It counts the records