Data Science Model Deployments and Cloud Computing on GCP - Lab - Monitoring and Spark UI

The video tutorial explains how to execute and monitor Pyspark jobs using Google Cloud Dataproc. It covers checking job status, debugging failures, and verifying output. The tutorial also discusses monitoring memory and CPU usage, exploring job stages through Spark UI, and introduces scheduling jobs using Airflow or Dataflow Composer for regular execution.

Explain the process of debugging a Pyspark job using the UI.

What will you learn in the next lecture regarding Dataproc Serverless jobs?