Apache Spark 3 for Data Engineering and Analytics with Python - Aggregations - Setting Up Flight Summary Data

The video tutorial guides viewers through preparing data for learning aggregations using flight summary data. It starts with creating a folder and uploading a CSV file, then moves to setting up a Jupyter Notebook for data analysis. The tutorial covers loading data into a DataFrame, counting rows, and renaming columns for clarity. It concludes with a brief overview of the data's structure and a preview of the next lesson on aggregations.

What is the first step in preparing the data for aggregation?

• Analyzing the data

• Renaming columns in the data

• Creating a new folder for the data

• Running the PySpark notebook

What is the purpose of creating a heading in the Jupyter notebook?

• To rename columns

• To count the data rows

• To reflect the task being performed

• To summarize the data

Which format is used to read the flight summary data into a DataFrame?

• JSON

• XML

• CSV

• Parquet

What is the significance of inferring the schema when loading data?

• It helps in renaming columns

• It automatically detects data types

• It counts the number of rows

• It ensures the data is read as a CSV

How many rows are present in the flight summary data?

• 4693

• 441

• 5000

• 10000

What does the 'count' column represent in the flight summary data?

• The number of passengers

• The number of airports

• The number of times a flight route has been used

• The number of flight routes

What is the new name given to the 'count' column?

• Route_Count

• Flight_Total

• Usage_Count

• Flight_Count