PySpark and AWS: Master Big Data with PySpark and AWS - Change Data Capture Pipeline

The video tutorial covers a data processing workflow using AWS services like DMS, S3, Lambda, and Glue. It begins with setting up and changing IDs for update, insert, and delete operations. The workflow is executed, and the results are verified by checking the updated data in S3. The tutorial concludes with final steps, data comparison, and resource cleanup. The course aims to provide a basic understanding of PySpark and AWS, encouraging learners to explore further on their own.

What ID was assigned to the updated row in the database?

• 5

• 11

• 7

• 9

Which row was deleted during the database changes?

• Row 12

• Row 10

• Row 7

• Row 5

Which AWS service is responsible for reading the newly landed file and merging data?

• Lambda

• Glue

• S3

• RDS

What triggers the Glue job in the data pipeline?

• DMS task

• Manual execution

• Lambda function

• S3 bucket update

What should be done with AWS services after completing the data pipeline?

• Restart them

• Terminate them

• Leave them running

• Ignore them

What is the purpose of the course as mentioned in the final section?

• To teach advanced AWS skills

• To focus on PySpark only

• To provide in-depth knowledge

• To offer basic understanding

What is the final step after verifying the data changes in the pipeline?

• Download the data

• Terminate AWS services

• Restart the pipeline

• Re-run the pipeline