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Run an Apache Spark Job with Dataproc

Apache Spark is a powerful open-source unified analytics engine for large-scale data processing. With its ability to handle big data and its integrated APIs for languages such as Python (PySpark), it is often used by data scientists for predictive analytics. One such application is the Monte Carlo simulation, a computational algorithm that relies on repeated random sampling to obtain numerical results. In this hands-on lab, you will create a managed Apache Spark cluster and leverage Monte Carlo simulations to predict the growth of an investment portfolio.

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Path Info

Level
Clock icon Beginner
Duration
Clock icon 45m
Last updated
Clock icon Sep 01, 2025

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Table of Contents

  1. Challenge

    Create a Managed Dataproc Cluster with Apache Spark Pre-Installed

    1. Enable the Dataproc API and navigate to the Dataproc console.
    2. Create a standard Dataproc cluster on Compute Engine.
    3. Configure the master and worker nodes with n2-standard-2 machine types.

  2. Challenge

    Using Python, Run a Monte Carlo Simulation That Estimates the Growth of a Stock Portfolio

    1. Connect to the Dataproc cluster's primary node via SSH.
    2. Start a PySpark Python interpreter.
    3. Run the Monte Carlo simulations.
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