Co-authored by HPE's Ron Reuben and Matt Hausmann
HPE Ezmeral delivers end-to-end analytics connecting enterprise data operations with ML Ops
Today, weโre excited to announce the availability of the latest offerings from HPE Ezmeral, the industryโs first cloud-native, unified analytics, and data lakehouse solution optimized for hybrid environments. HPE Ezmeral Data Fabric File and Object Store version 7.0, HPE Ezmeral Runtime Enterprise (formerly HPE Ezmeral Container Platform), Unified Analytics, and ML Ops version 5.4 are now generally available.
We refer to this release as our Enterprise Data and Unified Analytics release. By connecting data life cycle with ML Ops, customers can solve the challenges of global data analytics, data science, and data life cycle use cases, allowing them to deliver trusted insights that accelerate analytics across hybrid or multi-cloud architectures. With these newest capabilities, enterprises can:
- Centralize hybrid data types (including objects) into a high-performance, high-scale logical data store that eliminates data silos and increases data accuracy for more confident decisions.
- Optimize for analytics with an open, Kubernetes-based, Apache Sparkโข environment spanning across hybrid environments.
- Embrace the edge by enabling a data analytics platform environment that can span across edge to multi-cloud.
HPE Ezmeral Data Fabric for more effective enterprise data operations
HPE Ezmeral Data Fabric File and Object Store is the industryโs first data fabric to centralize S3 objects, files, streams, and databases in one scalable data platform from edge to cloud. HPE Ezmeral Data Fabric avoids data silos created by distributed data sources by ingesting hybrid data types into a single logical infrastructure, and then uses a single management view to centralize, manage, and control lifecycle processes.
Key new features:
- Object Store for Analytics
High performance object store optimizes all object sizes for performance and storage efficiency. Multi-protocol access increases object dataโs availability to traditional and cloud-native users and applications.
- Real-time and operational analytics
A multi-model NoSQL database with integrations with Apache Drill for distributed SQL queries supports real-time and operational analytics. Real-time data masks are applied in transit for database queries to hide sensitive and PII information while maintaining integrity of the original data.
- Ezmeral Ecosystem Pack refresh
Extensive updates for certified new versions of Apache and Hadoop components simplifies configuration, integration, and management of open-source components that work on top of HPE Ezmeral Data Fabric.
HPE Ezmeral Runtime Enterprise, Unified Analytics, and ML Ops
The HPE Ezmeral Runtime Enterprise 5.4 release introduces a number of important new feature enhancements tied to advancing our AI/analytics and DevOps capabilities. This latest version allows our clients to build their analytics factories faster and accelerate their data science initiatives by simplifying app modernization, improving data and model collaboration, enhancing policy management, and delivering out of the box run-time security.
The latest enhancements include:
- Enterprise data lakehouse on premises or hybrid
Customers can now deliver modern Apache Spark-based lakehouses with multi-version 24x7 support along with support for air-gap installations, ensuring total isolation for airtight security. Weโve also added integrations for enhanced metadata management and connectivity of external apps to call native Spark jobs. Additionally, users can create unified real-time and batch analytics with Delta Lake.
- Accelerated analytics
Weโve added select GPU enhancements to support multiple generations and NVIDIA Multi-Instance GPU (MIG) technology to accelerate more workloads and maximize efficiency. This acceleration allows data scientists and engineers to build, develop, and deploy analytics faster. Watch the video or read the tech paper.
- Streamlined analytic pipelines
Weโve enabled collaboration and simplified creation of end-to-end, analytic workflows with integrations with popular tools like Apache Airflow, ML Flow, Kale, and Kubeflow.
Expanded HPE GreenLake integration
With 76% of enterprises expecting to be using on-premises, third-party-managed private cloud infrastructure for data and analytics workloads within the next year, weโve also enhanced our integration with HPE GreenLake. In 2021 we delivered the HPE Ezmeral powered services โ HPE GreenLake for Containers and HPE GreenLake for ML Ops. Next, we introduced the HPE Ezmeral Ecosystem Program to accelerate the validation of ISV partners into the HPE GreenLake Marketplace. Since the program launched, weโve added 44 solution partners, and we're excited to announce 11 new ISV partners in the last quarter including: TigerGraph, vFunction, SingleStore, Kubecost, SAP, Replicated, Pepperdata, Confluent, Vertica, Cinchy, and Hazelcast.
Analytics in action
HPE Ezmeral helps enterprises unify, modernize, and analyze all their data, applications, and infrastructure from edge to cloud. This capability allows analytics and data science teams to scale Apache Spark lakehouses, speed advanced analytics workflows, and streamline the integration between Data Ops and ML Ops functions.
Wonder what this looks like in the real world? Our customers are our best examples:
BMW: The worldโs leading premium manufacturer of cars and motorcycles manages distributed data via a single platform to ensure a globally consistent cloud experience. To accelerate time to market, BMW uses HPE Ezmeral to analyze data collected from electric test cars across the globe. Data such as battery temperature, power dissipation, or vehicle speed is captured via analytics, and a data lakehouse built on HPE Ezmeral Data Fabric provides their data scientists and engineers universal and direct access to the data--no matter its locality. HPE Ezmeral also provides a global catalogue of analytics tools and data operations processes for analysis and simulation. As a bonus, BMW scientists and engineers are consuming this solution via the HPE GreenLake edge-to-cloud platform. Read the press release here.
Major healthcare provider: Consider a healthcare network with hospitals spanning multiple geographies and thousands of images, capturing machines like MRIs and x-ray systems. Their goal is to catch abnormalities early. To do that, each hospital and clinic location has several rooms with specialized equipment and highly skilled doctors and nurses. These x-ray and MRI systems are enhanced with AI so staff can apply pattern recognizing filters to patientsโ images in real time. All these locations and devices must talk to each other and rapidly process massive amounts of information to deliver insight to doctors, nurses, and patients.
In an edge-in distributed analytics stack, HPE Ezmeral works at the edge, the data center, and the public cloud to connect the data, infrastructure, and applications from edge to cloud. By virtualizing these stacks with HPE Ezmeral Runtime Enterprise and levering our HPE Ezmeral Data Fabric, HPE Ezmeral allows data professionals to manage their data, develop and deploy analytics, and monitor the entire solution from a single pane of glass. Watch this video to find out more about our edge to cloud solutions.
Whatโs coming next
As data and analytics are constantly evolving, enterprises must have a data analytics solution that is open, bringing together distributed architectures from edge to cloud. They must also be able to leverage their tools of choice and have the flexibility to follow their data no matter what path it takes. HPE Ezmeral is a hybrid data and analytics platform purpose-built to drive data-first modernizations, enabling enterprises to unlock the value of their data wherever it lives.
Here at HPE, weโre excited to see our latest product offerings hit the market and look forward to engaging with you to help you succeed. Keep an eye out for more HPE Ezmeral news as we deliver upcoming quarterly releases.
To learn more, please visit HPE Ezmeral, take free courses with HPE Ezmeral Learn on Demand, and read the technical launch blogs for HPE Ezmeral Data Fabric and HPE Ezmeral Runtime.
Ron Reuben and Matt Hausmann
About the authors:
Matt Hausmann is part of the HPE Ezmeral Product to Market team. (See bio above right on this page.)
Hewlett Packard Enterprise
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Matt_Hausmann
Over the past decades, Matt has had the privilege to collaborate with hundreds of companies and experts on ways to constantly improve how to turn data into insights. This continues to drive him as the ever-evolving analytics landscape enables organizations to continually make smarter, faster decisions.
