H46BHS | HPE Private Cloud AI: HPE AI Essentials for Data Analytics

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HPE Private Cloud AI: HPE AI Essentials for Data Analytics

H46BHS

Course ID	H46BHS
Duration	1 day
Format	ILT/VILT
Schedule, pricing & registration
Browse related courses

Overview

This course teaches you how to create and run data analytics applications on Apache Spark on HPE AI Essentials Software. It also demonstrates the usage of Ray Datasets for data analytics. The course is approximately 30% lecture and 70% practical hands-on labs.

Course ID	H46BHS
Duration	1 day
Format	ILT/VILT
Schedule, pricing & registration
Browse related courses

Audience

This course is ideal for system administrators, integrators, data analysts, and learners who want to implement HPE Private Cloud AI solution.

Prerequisites

Before attending this course, you should have:

An understanding of Kubernetes or any container orchestration software
A basic understanding of big data open-source tools and frameworks
A basic understanding of HPE GreenLake for File Storage

Objectives

After completing this course, you should be able to:

Describe the features and capabilities of HPE AI Essentials
Define the features and usage of Apache Spark and Ray
Demonstrate the application of HPE AI Essentials in business analytics
Build and deploy analytical applications using HPE AI Essentials

Course outline

Module 1: Introduction to HPE AI Essentials Software	Introduction to HPE AI Essentials: Features and capabilities Data analytics components Navigation How to get started
Module 2: Distributed Data Processing Overview	What Apache Spark is Components of a Spark-centric application What Spark supports Spark use cases HPE AI Essentials and Spark architecture Understanding Ray Features and usage of Spark Differences between Ray and Spark
Module 3: HPE AIE for Business Analytics	Types and usage of Spark images How to manage Spark applications Configure memory for Spark applications Interactive sessions for Spark applications Submitting statement process in HPE AI Essentials Spark History Server in HPE AI Essentials Using Spark SQL API in HPE AI Essentials Using whylogs with Spark Using whylogs with Ray Running Spark applications in Namespaces Define GPU support for Spark in HPE AI Essentials
Module 4: Build Applications Using HPE AIE	How to create and submit a wordcount Spark application Demonstrate financial time series workflow Identify MNIST digits recognition workflow in HPE AI Essentials Build and deploy analytical applications with Ray

5 reasons to choose HPE as your training partner

Learn HPE and in-demand IT industry technologies from expert instructors.
Build career-advancing power skills.
Enjoy personalized learning journeys aligned to your company’s needs.
Choose how you learn: in-person , virtually , or online —anytime, anywhere.
Sharpen your skills with access to real environments in virtual labs .

Explore our simplified purchase options, including HPE Education Learning Credits .

Lab outline

Lab 1: Getting started	Task 1: Log in Task 2: Add users
Lab 2: Features and Usage of Apache Spark and Ray	Task 1: Walkthrough of the platform Task 2: Observe functionality difference with Spark credentials Task 3: Creating a simple Spark application Task 4: Features and usage of Ray Datasets
Lab 3: Using HPE AIE for Business Analytics	Task 1: Managing Spark applications Task 2: Configuring memory for Spark applications Task 3: Interactive sessions in HPE AI Essentials Task 4: Process of submitting Statement in HPE AI Essentials Task 5: Working with Spark History Server in HPE AI Essentials Task 6: Using Spark SQL API in HPE AI Essentials Task 7: Using whylogs in Spark Task 8: Using whylogs with Ray
Lab 4: Building Applications using HPE AI Essentials	Task 1: Creating and submitting a wordcount Spark application Task 2: Demonstrating financial time series workflow Task 3: MNIST digits recognition workflow in HPE AI Essentials Task 4: Build and deploy analytical applications with Ray

Learn more

HPE.com/ww/learnbigdata

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Hewlett Packard Enterprise shall not be liable for technical or editorial errors or omissions contained herein.

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a50014266enw, H46BHS A.00, November 2025

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