PySpark and AWS: Master Big Data with PySpark and AWS - Spark DF (Count, Distinct, Duplicate)

This video tutorial covers essential DataFrame operations in Spark, focusing on filtering rows and columns, and using functions like count, distinct, and drop duplicates. The count function helps determine the number of rows, while distinct identifies unique rows. Drop duplicates allows for filtering based on specific columns, providing flexibility in data management. The tutorial emphasizes understanding these functions' applications and limitations in handling large datasets.

Can distinct be applied to specific columns in a data frame? Explain your answer.

How does the drop duplicates function differ from the distinct function?

What is the output of applying drop duplicates on a data frame with gender as the specified column?

Explain how to filter data based on multiple columns using drop duplicates.