PySpark and AWS: Master Big Data with PySpark and AWS - Writing Glue Shell Job

The video tutorial covers setting up a Glue job by merging imports from a Databricks notebook, creating a Spark session, and configuring S3 bucket paths. It explains the importance of managing S3 buckets to prevent unwanted Lambda function triggers. The tutorial also details the code logic for processing data and writing outputs, emphasizing the use of dynamic file paths. Finally, it concludes with a brief overview of the next steps, including spinning up TMS and RDS to replicate the pipeline.

What is the purpose of merging imports from the Databricks notebook with existing imports?

Explain how the bucket name and file name are extracted in the code.

Why is a new S3 bucket created instead of using the same bucket for input and output files?

What condition is checked to determine if the file name indicates a full load?

Describe the process of writing data back to the final file path.

How does the code handle updated data in the input file path?

What is the significance of the naming convention for the final directory in PySpark?