PySpark and AWS: Master Big Data with PySpark and AWS - Glue Job (Change Capture)

The video tutorial explains the process of setting up a Change Data Capture (CDC) pipeline. It covers reading and updating data from CSV files, handling data frames, and comprehending changes such as insertions, updates, and deletions. The tutorial also discusses using directories, including S3, for data storage and outlines the steps for loading full and change data. The final section focuses on preparing the final data output.

What is the first step in performing a CDC pipeline?

• Comprehending the changes

• Loading the full data

• Writing data back to the file

• Loading the change data

What is the purpose of the 'action' column in the data frame?

• To store the full name

• To hold the IDs

• To indicate the type of change

• To list the cities

How do you specify the storage location for the updated file?

• By saving it in the same directory

• By using a cloud storage service

• By specifying the S3 directories

• By using a local directory

What is the final step in integrating the updated data frame?

• Comprehending the changes

• Reading data from the directory

• Displaying the data with headers

• Writing the updated data back to the final output

What does FFDP stand for in the context of the video?

• Full File Data Process

• Final File Data Frame

• Full Frame Data Path

• Final File Data Process