PySpark and AWS: Master Big Data with PySpark and AWS - Creating DMS Destination Endpoint

The video tutorial guides viewers through the process of creating a destination endpoint for an S3 bucket using AWS DMS. It covers setting up an IAM role with the necessary permissions to allow the endpoint to access the S3 bucket. The tutorial also explains how to configure the role and attach the appropriate policies, and concludes with testing and finalizing the endpoint setup.

What is the first step in creating a destination endpoint for an S3 bucket?

• Attach policies to the IAM role

• Search for S3 in the console

• Create a new IAM role

• Search for RDS in the console

What is the endpoint identifier changed to when creating a target endpoint?

• IAM Role Access

• DMS Full Access

• CDCS 3 by Spark

• CDC RDS MySQL

Why is it necessary to create an IAM role for the DMS endpoint?

• To provide full access to all AWS services

• To allow the endpoint to access the S3 bucket

• To create a new S3 bucket

• To enable the endpoint to access RDS

What permissions are typically granted to the IAM role for accessing S3?

• Read-only access

• Write-only access

• No access

• Full access

What is a good convention when organizing data within an S3 bucket?

• Avoid using folders altogether

• Store all data in a single folder

• Create multiple buckets for different purposes

• Use a single bucket with multiple folders

What should be done after creating the IAM role and attaching the policy?

• Delete the role

• Test the connection

• Create a new S3 bucket

• Create a new RDS instance

What is the purpose of testing the connection in the endpoint settings?

• To ensure the endpoint can access the S3 bucket

• To check if the RDS instance is running

• To confirm the S3 bucket is empty

• To verify the IAM role is deleted