Snowflake - Build and Architect Data Pipelines Using AWS - Lab – Set Up Airflow DAG

The video tutorial guides viewers through deploying a DAG on AWS Airflow, focusing on using the Snowflake operator. It covers setting up a Snowflake query, configuring DAG tasks, and executing and monitoring the DAG. The tutorial emphasizes the importance of using application credentials and provides a step-by-step demonstration of the process, concluding with a brief overview of the next steps in the course.

What is essential to include in the requirements file to ensure the Snowflake operator functions correctly?

• SQLAlchemy

• Airflow CLI

• Snowflake Python connector

• AWS SDK

What is the primary role of the first task in the DAG?

• To create a new database

• To send an email notification

• To copy data into Snowflake

• To execute a Python script

Which function is used to trigger the Glue job in the DAG?

• execute_glue_task

• start_glue_job

• execute_job

• trigger_glue

What should be done before deploying the Python script to ensure it appears in Airflow?

• Restart the Airflow server

• Compile the script

• Create a new Airflow project

• Drag and drop the script into the DAGs folder

How can you monitor the progress of the DAG tasks?

• By running a SQL query

• By clicking on logs list in the Airflow UI

• By using the Airflow CLI

• By checking the system logs