PySpark and AWS: Master Big Data with PySpark and AWS - Full Load Pipeline

The video tutorial covers setting up a Data Migration Service (DMS) to perform a full load of data into an S3 bucket. It explains the process of configuring and starting a data migration task, ensuring the RDS instance is running, and verifying data integrity. The tutorial also demonstrates restarting the task for a full load and ongoing replication, triggering a Lambda function, and executing an AWS Glue job for data processing. The video concludes with data verification and a summary of the process.

What is the initial step in setting up the Data Migration Service (DMS) for a full load?

• Configure the MySQL Workbench

• Set up a Lambda function

• Create a new S3 bucket

• Start the RDS instance

Before starting the data migration task, what must be ensured about the RDS instance?

• It is up and running

• It is deleted

• It is stopped

• It is in maintenance mode

What is the consequence of choosing 'resume' instead of 'restart' for the data migration task?

• It will perform a full load again

• It will stop the task

• It will only detect changes

• It will delete the existing data

What triggers the AWS Glue job in the data migration process?

• A new file in the S3 bucket

• Stopping the data migration task

• Completion of the full load

• Starting the RDS instance

What happens to the data when the DMS task is restarted after a database update?

• The data is deleted

• The updated data is considered as the fresh state

• The data is duplicated

• The previous data is retained

What is the role of the Lambda function in the data migration process?

• To delete the S3 bucket

• To update the MySQL database

• To trigger the AWS Glue job

• To start the RDS instance

What is verified in the final step of the data migration process?

• The deletion of the S3 bucket

• The presence of the full load in the output directory

• The status of the RDS instance

• The configuration of the MySQL Workbench