PySpark and AWS: Master Big Data with PySpark and AWS - Creating Glue Job

This video tutorial guides viewers through the process of creating and configuring an AWS Glue job. It covers the setup of IAM roles, specifying job details such as type and version, and finalizing the job setup. The tutorial also highlights the differences between running Pyspark jobs in AWS Glue and other environments like Databricks. The video concludes with a brief mention of integrating the Glue job with a Lambda function in a subsequent video.

What is the primary difference between running a PySpark job in AWS Glue and other environments?

• AWS Glue is only for data storage.

• AWS Glue does not support PySpark.

• AWS Glue jobs are faster than local jobs.

• AWS Glue requires a specific configuration setup.

What is the first step in creating a Glue job in AWS?

• Writing the PySpark code.

• Searching for AWS Glue in the console.

• Creating an S3 bucket.

• Setting up a local environment.

Why is it necessary to create an IAM role for a Glue job?

• To increase the speed of the job.

• To allow Glue to run on local machines.

• To enable Glue to access S3 buckets.

• To reduce the cost of running the job.

Which permissions are essential for the IAM role associated with a Glue job?

• EC2 full access and S3 full access.

• CloudWatch full access and S3 full access.

• Lambda full access and DynamoDB full access.

• RDS full access and CloudFront full access.

What type of job is selected for the Glue job in this tutorial?

• Spark streaming job.

• Python shell job.

• Simple Spark job.

• Machine learning job.

What is the minimum number of workers required for the Glue job?

• 10

• 1

• 5

• 2

What is the purpose of writing a new script for the Glue job?

• To ensure the job runs without errors.

• To avoid using any scripts.

• To customize the job according to specific needs.

• To use pre-written AWS scripts.