HPE Ezmeral

HPE Ezmeral is a hybrid analytics and data science platform designed to drive data-first modernizations, enabling enterprises to unlock the value of their data wherever it lives. HPE Ezmeral powers HPE GreenLake analytic services to help customers unify, modernize, and analyze all their data across edge-to-cloud.

The complete HPE Ezmeral portfolio offers integrated workflows from analytics to ML/AI, spanning hybrid clouds and edge locations. HPE Ezmeral Runtime Enterprise provides a software orchestration platform designed to deploy modern applications using open source Kubernetes from edge to cloud. With HPE Ezmeral ML Ops, you can operationalize end-to-end processes and speed up data modeling timelines. HPE Ezmeral Data Fabric enables you to securely access data wherever it is located, from edge to cloud. And HPE Ezmeral Unified Analytics integrates security-hardened enterprise-grade open source Apache Spark™ to ensure as-needed data portability. This offers you a unified, modern analytics and data lakehouse platform optimized for on-premises, edge and cloud analytic deployments.

Learn more about the HPE Ezmeral products

HPE Ezmeral Runtime Enterprise – (formerly known as the HPE Ezmeral Container Platform) is a secure, enterprise-grade platform to build and deploy cloud-native and non-cloud-native (i.e., legacy) applications at scale across data centers, multiple clouds, and at the edge for a wide range of use cases. It provides all the tools enterprise customers need to build, modernize, deploy, monitor and manage a wide range of AI and analytics workloads to unleash their data’s full potential and accelerate their data-driven digital transformation.

HPE Ezmeral ML Ops – is a software solution that extends the capabilities of HPE Ezmeral Runtime Enterprise to support the entire machine learning (ML) lifecycle. HPE Ezmeral ML Ops is an end-to-end data science solution with the flexibility to run on-premises, in multiple public clouds, or in a hybrid model, and respond to dynamic business requirements in a variety of use cases. HPE Ezmeral ML Ops addresses the challenges of operationalizing ML models at enterprise scale by delivering a cloud-like experience combined with pre-packaged tools to operationalize the machine learning lifecycle.

HPE Ezmeral Data Fabric File & Object Store – is the industry’s first edge-to-cloud solution that ingests, stores in the native format, and completes in-place processing of different data types. It supports the most popular data and analytics APIs, which simplifies data access across the enterprise for all analytics users.

HPE Ezmeral Unified Analytics – features native integration of Apache Spark Kubernetes Operator and offers a unified data experience to securely connect to data wherever it exists.

HPE Ezmeral supports the most popular open-source ML Ops, Airflow, MLflow, and Kubeflow projects while integrating with an ever-growing ecosystem of certified HPE ISV partners.

The associated BlueK8s open source initiative will include a number of projects to help bring enterprise-level capabilities for distributed stateful applications to Kubernetes. The first open source project in this initiative is Kubernetes Director, or KubeDirector for short. (See Projects for specifics).


HPE Ezmeral demo

Automated installation for HPE Ezmeral Runtime Enterprise and HPE Ezmeral ML Ops on various platforms for demo purposes.

HPE Ezmeral Python Library

Python library and Command Line Interface (CLI) for HPE Ezmeral Runtime Enterprise and HPE Ezmeral ML Ops.

Learn from the experts

Note: The HPE Ezmeral Container Platform referenced in these videos has been renamed to HPE Ezmeral Runtime Enterprise. All other information is correct.

Interactive Demo Experience (IDE)

Use the HPE Ezmeral interactive demo experience to learn how to perform common tasks related to building and deploying stateful, containerized applications with HPE Ezmeral Runtime Enterprise, building an Apache Spark ML model, and protecting container data using data fabric.



Kubernetes Director (aka KubeDirector) for deploying and managing stateful applications on Kubernetes.

Apache Spark

HPE Ezmeral software provides the option of leveraging the power of Apache Spark, which is an open-source unified analytics engine for large-scale data processing that is maintained by the Apache Software Foundation. If your organization has a need to run Apache Spark jobs in a Containerized environment, such as within a Kubernetes cluster using a Spark Operator or Livy API server to manage Apache Spark jobs, then HPE offers the HPE Ezmeral Unified Analytics license for the HPE Ezmeral Runtime Enterprise platform.

Apache® and Apache Spark™ are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by the Apache Software Foundation is implied by the use of these marks.


Take advantage of our free, Jupyter-Notebook based workshops available in the HPE Developer Hack Shack. These technical workshops provide you with an in-depth, hands-on learning experience where you can interact with and learn from the experts. Designed to fit your schedule, these workshops are available 24/7 – any time, from anywhere. HPE Ezmeral Runtime Enterprise workshops (previously called HPE Ezmeral Container Platform workshops) are available today.

Any questions on HPE Ezmeral Runtime Enterprise?

Join the HPE Developer Slack Workspace and start a discussion in our #ezmeral-runtime-enterprise channel.

Not a Slack user? You can also ask your questions in our HPE Ezmeral Forum.

Related Blogs

Srikanth Venkata Seshu

Highlighting key features of HPE Ezmeral Runtime Enterprise Release 5.4

Mar 31, 2022
Cenz Wong

Mapping Kubernetes Services to HPE Ezmeral Runtime Enterprise Gateway

Dec 6, 2021
Vinothini Raju

Autopilot Kubernetes Deployments on HPE Ezmeral Runtime Enterprise

Nov 23, 2021
Ka Wai Leung & Jason Mashak

Secure containerized and traditional apps concurrently

Sep 30, 2021
Cenz Wong

Data Analytics with PySpark using HPE Ezmeral Container Platform

Sep 7, 2021
Cenz Wong

Getting Started with DataTaps in Kubernetes Pods

Jul 6, 2021
Don Wake

On-Premise Adventures: How to build an Apache Spark lab on Kubernetes

Jun 15, 2021
Cenz Wong

Ways to interact with Kubernetes Clusters managed by HPE Ezmeral Container Platform

May 28, 2021
Sahithi Gunna

Application Modernization with the Application Workbench

May 10, 2021
Dale Rensing

Open Source Contributor Explains How KubeDirector Empowers Data Intensive Apps

Mar 18, 2021

Boost Your Analytics Factory into Hyperdrive

Mar 9, 2021
Raz Haleva

The Challenges of Sharing GPUs and How to Solve Them

Feb 3, 2021
Dale Rensing

Exploring Data Fabric and Containers in HPE DEVs new Munch & Learn monthly gatherings

Jan 28, 2021
Ellen Friedman

Making AI a Reality

Jan 15, 2021
Joel Baxter & Kartik Mathur & Don Wake

Building Dynamic Machine Learning Pipelines with KubeDirector

Aug 14, 2020
Prashant Sachdeva

HPE achieves gold for large-scale enterprise Kubernetes deployments

Jun 17, 2020
Denis Choukroun

HPE Container Platform REST API – Part 2: Deploying containerized applications

Jun 4, 2020
Denis Choukroun

HPE Container Platform REST API – Part 1: Authenticating

May 26, 2020
Dale Rensing

App DEV and the HPE Container Platform

Apr 9, 2020
Marcel Jakob

Second internal HPE Hackathon around IoT and AI

Mar 5, 2020
Dale Rensing

Think container first

Dec 5, 2019
Tom Phelan

Running Non-Cloud-Native Apps on Kubernetes with KubeDirector

Nov 18, 2019
Tom Phelan

KubeDirector: The easy way to run complex stateful applications on Kubernetes

Sep 9, 2019
Tom Phelan & Joel Baxter

Deploying Complex Stateful Applications on Kubernetes with KubeDirector

Sep 9, 2019
Tom Phelan

Complex Stateful Applications on Kubernetes: KubeDirector version 0.2

Sep 9, 2019
Didier Lalli

Say NO to (Cloud) Vendor Lock-in!

Sep 3, 2019

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