Apache Spark 3 for Data Engineering and Analytics with Python - Aggregations

The video tutorial explains how to perform data aggregations in big data analytics using Spark. It covers the importance of aggregation functions like sum and max, and demonstrates their application through examples involving car price data. The tutorial concludes with a brief overview of the concepts and encourages viewers to apply these techniques using Spark.

What is the primary purpose of using aggregations in big data analytics?

• To increase the size of the data set

• To summarize and extract meaningful insights from the data

• To delete unnecessary data

• To convert data into a different format

Which of the following is NOT a common aggregation function provided by Spark?

• Multiply

• Average

• Max

• Sum

In the example provided, what is the first step to calculate the sum of car prices per year?

• Convert prices to a different currency

• Apply the Max aggregation function

• Select the price and year, then group by year

• Delete all records except for the year 2020

What is the stock value for the year 2020 after applying the sum aggregation?

• 110,000

• 65,000

• 310,000

• 200,000

How can you determine the maximum price recorded in a given year using Spark?

• By using the Average aggregation function

• By using the Max aggregation function

• By using the Sum aggregation function

• By using the Count aggregation function