Course data sheet
HPE Private Cloud AI: HPE AI Essentials for Data Engineering
H46BGS
Table of Contents
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.
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
Course outline
| Module 1: Introduction to HPE AI Essentials Software |
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| Module 2: Introduction to EzPresto |
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| Module 3: Introduction to Workflows |
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| Module 4: Superset Overview |
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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.
- 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 |
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| Lab 2: Running Federated Queries Across Various Data Sources |
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| Lab 3: Authoring and Monitoring — Workflows and Data Pipelines |
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| Lab 4: Batch and Stream ETL with Apache Spark |
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| Lab 5: Orchestrating Spark Applications |
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| Lab 6: ETL and Visualize Data from Spark Application |
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| Lab 7: ETL Using Custom Data Sources |
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a50014267enw, H46BGS A.00, November 2025