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HPE Private Cloud AI: HPE AI Essentials for Data Engineering

H46BGS

Course ID

H46BGS

Duration

1 day

Format

ILT/VILT

Schedule, pricing & registration

Browse related courses

Overview

This course demonstrates authoring and monitoring workflows and data pipelines using HPE AI Essentials Software. It also introduces EzPresto, a SQL query engine, and covers how to use data visualizations and dashboards using Apache Superset. In addition, you learn about data pipeline management using Apache Airflow and Ray framework on HPE AI Essentials.


The course comprises 30% lectures and 70% practical hands-on labs.

Course ID

H46BGS

Duration

1 day

Format

ILT/VILT

Schedule, pricing & registration

Browse related courses

  • Audience

    This course is ideal for system administrators, integrators, data engineers, and learners who wants to implement HPE AI Essentials 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
    • Demonstrate running federated queries across various data sources using HPE AI Essentials
    • Demonstrate authoring and monitoring workflows and data pipelines using HPE AI Essentials
    • Use data visualizations and dashboards with HPE AI Essentials
Divider

  • Course outline

Module 1: Introduction to HPE AI Essentials Software
  • Introduction to HPE AI Essentials:
    • Features and capabilities
    • Data engineering components
    • Navigation
    • How to get started

Module 2: Introduction to EzPresto
  • Understand what EzPresto is
  • Discuss EzPresto key features
  • Recognize EzPresto architecture
  • Define connect data sources using HPE AI Essentials
  • Describe steps to connect to external applications through JDBC using HPE AI Essentials
  • Use Spark to query EzPresto on HPE AI Essentials
  • Discuss connectivity to EzPresto via Python client using HPE AI Essentials
  • Define cache data

Module 3: Introduction to Workflows
  • Define Airflow functionality
  • Recognize Airflow architecture and its components
  • Configure Airflow DAGs Git repository using HPE AI Essentials
  • Demonstrate Airflow configuration using HPE AI Essentials

Module 4: Superset Overview
  • Define Superset
  • Demonstrate BI reporting using Superset on HPE AI Essentials
  • Demonstrate retail store analysis dashboard using Superset on HPE AI Essentials

5 reasons to choose HPE as your training partner

  1. Learn HPE and in-demand IT industry technologies from expert instructors.
  2. Build career-advancing power skills.
  3. Enjoy personalized learning journeys aligned to your company’s needs.
  4. Choose how you learn: in-person , virtually , or online —anytime, anywhere.
  5. 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 with HPE AI Essentials
  • Task 1: Log in to HPE AI Essentials
  • Task 2: Add user

Lab 2: Running Federated Queries Across Various Data Sources
  • Task 1: Exploring Data Sources
  • Task 2: Connecting External Applications to EzPresto via JDBC
  • Task 3: Submitting Presto Queries from Notebook

Lab 3: Authoring and Monitoring — Workflows and Data Pipelines
  • Task 1: Airflow DAGs Git repository Configuration
  • Task 2: Configuring Airflow

Lab 4: Batch and Stream ETL with Apache Spark
  • Task 1: Batch ETL with Apache Spark
  • Task 2: View output
  • Task 3: Stream ETL with Apache Spark

Lab 5: Orchestrating Spark Applications
  • Task 1: Preparing the environment
  • Task 2: Running DAG for Spark Application
  • Task 3: Verify DAG Output

Lab 6: ETL and Visualize Data from Spark Application
  • Task 1: Getting the environment ready
  • Task 2: Running DAG for Spark Application
  • Task 3: ETL and Visualisation in Jupyter

Lab 7: ETL Using Custom Data Sources
  • Task 1: Pull and install custom MySQL image
  • Task 2: Accessing the database and listing tables

Learn more

HPE.com/ww/learnbigdata

© Copyright 2025 Hewlett Packard Enterprise Development LP. The information contained herein is subject to change without notice. The only warranties for Hewlett Packard Enterprise products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty.

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

All third-party marks are property of their respective owners.

a50014267enw, H46BGS A.00, November 2025

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