Data Science Model Deployments and Cloud Computing on GCP - Lab - Airflow with Serverless PySpark

This video tutorial explains how to set up and configure an Airflow DAG to schedule a PySpark job in a Dataproc serverless environment. It covers the creation of a Google Cloud Composer environment, the structure and components of the DAG script, and the process of uploading and executing the DAG. The tutorial also addresses troubleshooting common issues and re-running the DAG to ensure successful execution.

What is the importance of specifying the Python file location in the DAC script?

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What happens if one of the tasks in the DAG fails?

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What steps are involved in uploading the DAC script to the DAGS folder?

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Describe the output of the Pyspark job after it runs successfully.

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