Spark Programming in Python for Beginners with Apache Spark 3 - Aggregating Dataframes

The video tutorial covers the concept of aggregations in Apache Spark, focusing on three main types: simple, grouping, and windowing aggregations. It explains how these aggregations are implemented using built-in functions in Spark, such as average, count, max, min, and sum. The tutorial provides examples of simple aggregations, like summarizing a data frame, and grouping aggregations using SQL expressions and data frame methods. An exercise is given to practice creating a data frame with specific aggregations, and the video concludes with a preview of windowing aggregates.

What are the three main categories of aggregations in Apache Spark?

• Initial, Intermediate, and Final

• Basic, Advanced, and Complex

• Simple, Grouping, and Windowing

• Primary, Secondary, and Tertiary

Which function would you use to count all records in a DataFrame, including those with null values?

• sum(column_name)

• count(column_name)

• count(*)

• countDistinct(column_name)

What is the difference between count(*) and count(1) in Spark?

• count(*) counts all rows, count(1) counts only non-null rows

• count(1) is used for distinct counts

• count(*) and count(1) are equivalent in Spark

• count(*) is faster than count(1)

How can you group data by specific columns in Spark?

• Using the join method

• Using the groupBy method

• Using the filter method

• Using the select method

Which method allows you to run SQL expressions on a DataFrame in Spark?

• sqlExpr

• selectExpr

• groupExpr

• filterExpr

What is the purpose of the add transformation in Spark?

• To add new columns to a DataFrame

• To apply a list of aggregation functions

• To join two DataFrames

• To filter rows based on a condition

In the exercise, what is the first aggregate you need to compute?

• Total quantity

• Number of unique invoices

• Total value

• Average price