PySpark and AWS: Master Big Data with PySpark and AWS - Creating Lambda Function and Adding Trigger

This video tutorial explains how to set up a Glue PySpark job with a Lambda function in AWS. It covers creating a Lambda function, setting up IAM roles, configuring triggers, and testing the setup. The tutorial emphasizes using Python for Lambda functions and details the necessary permissions and configurations for seamless integration with S3 and Glue services.

What is the primary role of the Lambda function in the integration with Glue?

• To store data in S3

• To trigger a Glue job when a file lands in S3

• To monitor S3 bucket size

• To delete files from S3

Which programming language is commonly used for writing Lambda functions?

• Java

• C++

• Python

• Ruby

What is the purpose of creating an execution role for a Lambda function?

• To define the memory allocation

• To grant permissions to access other AWS services

• To set the timeout duration

• To specify the runtime environment

Which policy is NOT required for the IAM role associated with the Lambda function?

• S3 full access

• EC2 full access

• Glue console full access

• CloudWatch full access

Why is CloudWatch full access necessary for the Lambda function?

• To allow the function to log and monitor execution

• To enable real-time data processing

• To manage user authentication

• To provide network security

What is the significance of specifying a prefix or suffix in the Lambda trigger configuration?

• To increase the execution speed

• To reduce storage costs

• To enhance security

• To filter events based on file names

What should be done if the same S3 bucket is used for both input and output in a Lambda function?

• It is recommended and should be done

• It is not recommended but can be done with caution

• It requires special permissions

• It is prohibited by AWS