Data Science Model Deployments and Cloud Computing on GCP - Persistent History Cluster

This video tutorial covers the prerequisite steps for deploying a PySpark batch job on Dataproc Serverless. It includes setting up necessary variables, enabling BigQuery and Dataproc APIs, creating a subnet for private IP access, defining cluster and bucket variables, and creating a storage bucket. The tutorial also guides on creating a Dataproc cluster with a component gateway for persistent history storage.

What are the prerequisite steps before deploying a Pyspark batch job on Dataproc serverless?

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What variables are defined in the bash file for the Pyspark job?

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Why is it necessary to enable the Big Query API and Dataproc API?

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What is the significance of creating a subnet for Pyspark jobs on Dataproc serverless?

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What command is used to create a Dataproc cluster for persistent history storage?

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What is the purpose of the component Gateway in the Dataproc cluster?

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What should you do after the Dataproc single node cluster has been provisioned?

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