Snowflake - Build and Architect Data Pipelines Using AWS - Lab - Deploy a PySpark Transformation job in AWS Glue

The video tutorial guides viewers through the process of copying data from S3 to a table, using PySpark and Snowflake. It covers setting up the Spark session, writing SQL commands, creating DataFrames, performing data transformations, and executing inner joins. The tutorial also explains how to aggregate data and write the results to a Snowflake table. Finally, it demonstrates configuring and running the job in AWS Glue, including setting parameters like the number of workers and job timeout.

What is the primary purpose of the initial SQL commands in the PySpark job?

• To update the schema of the tables

• To create new tables in Snowflake

• To select specific columns from existing tables

• To delete data from the tables

Which method is used to read data into a PySpark data frame with specific conditions?

• Using the DB table parameter

• Using a custom query with the query keyword

• Using a direct table read

• Using a JSON file

What type of join is performed between the orders and line items data frames?

• Right join

• Inner join

• Full outer join

• Left join

What is the purpose of aggregating data by ship mode and ship date?

• To sort the data alphabetically

• To create a backup of the data

• To calculate the total price and count of orders

• To filter out unnecessary data

What is the final step in executing the PySpark job in AWS Glue?

• Exporting the data to a CSV file

• Deleting the existing data

• Running the job to write results into a Snowflake table

• Creating a new database