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 initial step in setting up the Glue job as mentioned in the video?

• Setting up a Lambda function

• Merging imports from Databricks notebook

• Writing data to a directory

• Creating a new S3 bucket

How is the Spark session named in the video?

• RDD

• CDC

• Lambda

• DataFrame

Why is a new S3 bucket created in the process?

• To increase storage capacity

• To avoid triggering the Lambda function again

• To separate input and output data

• To store temporary files

What indicates a full load file in the naming convention?

• The presence of 'load' in the file name

• A specific file size

• A unique file extension

• A timestamp in the file name

What is the main logic applied to the updated data frame?

• Deleting old data

• Compensating and writing back updated data

• Archiving data

• Transforming data into JSON format

How does Spark handle output files in terms of structure?

• As a single large file

• As a directory with partitioned files

• As a compressed archive

• As multiple CSV files

What is the usual convention for referring to the final directory in PySpark?

• As a table

• As a bucket

• As a database

• As a file