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Running a Spark Job on a Dataproc Cluster

Google Cloud Dataproc is a fully managed and highly scalable service capable of running Apache Spark as well as over 30 other open-source tools and frameworks. In this hands-on lab, you’ll provision a Dataproc cluster and then submit a Spark job that calculates and outputs the value of Pi to a high degree.

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Labs

Path Info

Level
Clock icon Intermediate
Duration
Clock icon 45m
Last updated
Clock icon Aug 31, 2025

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Table of Contents

  1. Challenge

    Enable the Necessary API

    Enable the Dataproc API, either through the user interface or the Cloud Shell.

  2. Challenge

    Create a Dataproc Cluster

    Provision a Dataproc cluster capable of running the Spark job.

  3. Challenge

    Submit a Spark Job

    Submit the Spark job, and evaluate the results.

Pluralsight Skills - Labs - Pluralsight
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Pluralsight Skills

Pluralsight Skills gives leaders confidence they have the skills needed to execute technology strategy. Technology teams can benchmark expertise across roles, speed up release cycles and build reliable, secure products. By leveraging our expert content, skill assessments and one-of-a-kind analytics, keep up with the pace of change, put the right people on the right projects and boost productivity. It's the most effective path to developing tech skills at scale.

What's a lab?

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

Provided environment for hands-on practice

We will provide the credentials and environment necessary for you to practice right within your browser.

Guided walkthrough

Follow along with the author’s guided walkthrough and build something new in your provided environment!

Did you know?

On average, you retain 75% more of your learning if you get time for practice.
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