Welcome to the next Around the IT Block Chalk Talk. I'm your host and
chalkboard writer the IT Oddfather Calvin Zito. In this Chalk Talk,
I’m going to give you an overview of HPE Ezmeral and talk about what’s new.
Yes, HPE is moving quickly to be the Edge-to-cloud platform and
HPE Ezmeral is a part of it. Our strategic imperative is to help
organizations like yours bring speed and agility to your most critical
data-driven workloads and workflows. HPE GreenLake edge-to-cloud platform enables you to connect,
protect, analyze and act on your data everywhere, edge-to-cloud, with the cloud that comes to you.
Customers struggle with three strategic initiatives:
• Unifying data and modernization apps: How to get a global data
infrastructure for the future business state? • Enabling analytics teams at enterprise scale:
How to scale up Apache Spark lakehouses with a cost-effective hybrid model?
• Avoiding lock-in while having valid choices: How to apply ML and AI to business processes faster?
Data engineers, analytics users, and data scientists have a diversity of tools and
this creates data challenges and tradeoffs and ultimately
massive diversity of siloed data sets and types.
HPE GreenLake cloud services enables enterprise to overcome these challenges and trade-offs.
Built from the ground up to be open and cloud-native,
our new HPE GreenLake analytics and data cloud services will help enterprises unify,
modernize, and analyze all of their data, from edge-to-cloud, in any and every place it's stored.
Available on the HPE GreenLake edge-to-cloud platform,
HPE Ezmeral is the first cloud-native unified analytics and data lakehouse solution
optimized for hybrid environments. This industry leading solution unifies data from edge-to-cloud
and modernizes apps, helping you achieve a consistent experience across data science,
analytics and data engineering teams, and allows you the freedom and flexibility
to choose the deployment environment across on-premises, cloud, and edge.
Let’s look at a high-level overview of HPE Ezmeral.
• Embraces data and intelligence from edge to cloud with a built-in data fabric to provide a
persistent store capability for stateful workloads • Open software platform, built on industry
standards with HPE innovations - Kubernetes based container framework and innovative HPE
orchestration so you can easily manage it all. • Optimized for data, analytics, and modern
applications with the agility and elasticity that you need.
I want to spend the rest of the Chalk Talk looking at the HPE Ezmeral product portfolio.
There’s a lot to talk about so hold on and let’s get into it!
I’ll briefly introduce the portfolio and then dive into each piece.
With HPE Ezmeral Solutions on HPE GreenLake: • Modernize your legacy data and
legacy apps with HPE Ezmeral Runtime. • Scale up Apache Spark lakehouses with the HPE
Ezmeral Unified Analytics, the industry’s first hybrid lakehouse solution for analytics and AI/ML.
• Speed up data science ML workflows with HPE Ezmeral MLOps, intregrated ML workflows
for easy data science team collaboration. • And underpinning it all is HPE Ezmeral Data
Fabric File and Object Store, the industry’s first edge to cloud data fabric optimized for analytics.
Let’s take a deeper look now starting with HPE Ezmeral Data Fabric and the new Object
Store – the first Kubernetes-native object store optimized for high-performance analytics. It
combines S3-native object stores, files, streams, and databases in one scalable data platform.
Available on bare metal and Kubernetes-native deployments, the new HPE Ezmeral Object Store
provides a global view of an enterprise’s dispersed data assets - unified access to
all data within a cloud-native model, securely accessible to the most demanding applications.
This warrants its own Chalk Talk because it is unique and highly differentiates HPE Ezmeral.
Next, let’s look at HPE Ezmeral Runtime – develop
and manage container-based apps and provide a complete open-Source
K8s based solution to ensure a fully supported solution that you can deploy without risk.
It helps reduce costs, resources, and time across development, management, and deployment. And it’s
a consistent deployment and management experience across edge, public cloud or on-premises.
New to the portfolio is HPE Ezmeral Unified Analytics, the first cloud-native,
modern analytics and data lakehouse platform. Customers were previously
limited to hyperscale environments requiring all your data to live in a public cloud
but with HPE Ezmeral Unified Analytics, it’s optimized for on-premises deployment
and ensures as-needed data portability. On-premises Apache Spark workloads offer
the freedom to choose deployment environments, tools, and partners needed to innovate faster.
Integrated workflows from analytics to ML/AI span hybrid clouds and edge locations,
including support for open-source ML Ops, Airflow, ML Flow, and Kubeflow technologies.
Lastly, let’s look at end-to-end automation of large-scale machine learning projects with HPE
Ezmeral MLOps. HPE Ezmeral MLOps delivers DevOps-like agility and a cloud-like
experience to operationalize the machine learning lifecycle from pilot to production.
There are pre-configured notebooks for consistent UX across ML frameworks and built-in security,
access control, and self-service sandboxes.
HPE Ezmeral, available on HPE GreenLake or standalone is the first
cloud-native unified analytics and data lakehouse solution
optimized for hybrid environments, dare I call it cloud native-hybrid!
I’ve covered a lot in a short time. To learn more, go to hpe.com/Ezmeral
Would love to hear from you on Twitter where you can find me as @CalvinZito.
You can find our blogs at community.hpe.com. Until next time, thanks for join me!