HPE GreenLake edge-to-cloud platform rolls out industry’s first cloud-native unified analytics and data lakehouse cloud services optimized for hybrid environments

Built on HPE Ezmeral software, analytics and data science teams benefit from frictionless access to data from edge to cloud and a unified platform for accelerated Apache Spark and SQL

In the Age of Insight, data has become the heart of every digital transformation initiative in every industry, and data analytics has become critical to building successful enterprises. Simply put, data drives competitive advantage.  However, for most organizations, significant challenges remain for organizations to successfully execute data-first modernization initiatives. Until now, organizations have been stuck with legacy analytics platforms that were either built for a pre-cloud era and lack cloud-native capabilities, or require complex migrations to public clouds, risking vendor lock-in, high costs and forcing adoption of new processes. This situation has left the big data and analytics software market — which IDC forecasts will reach $110 billion by 2023 – ripe for disruption.

HPE announced two disruptive HPE GreenLake cloud services that will enable customers to overcome these trade-offs.  There are four big value propositions we optimized for:
1.     Seamless experience for a variety of analytics, SQL, and data science users

2.     Top-notch performance

3.     Choice and open ecosystem by leveraging pure open source in a hybrid environment

4.     An intense focus on reducing TCO by up to 35% for many of the Workloads we are targeting

Built from the ground up to be open and cloud-native, our new HPE GreenLake for analytics 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. Now analytics and data science teams can leverage the industry’s first cloud-native solution on-premises, scale up Apache Spark lakehouses, and speed up AI and ML workflows. Today’s news is part of a significant set of new cloud services for the HPE GreenLake edge-to-cloud platform, announced today in a virtual launch event from HPE. The new HPE GreenLake for analytics cloud services include the following:

HPE Ezmeral Unified Analytics

HPE now offers an alternative to customers previously limited to solutions in a hyperscale environment by delivering modern analytics on-premises, enabling up to 35% more cost efficiencies than the public cloud for data-intensive, long running jobs typical in mission critical environments. Available on the HPE GreenLake edge-to-cloud platform, HPE Ezmeral Unified Analytics is the industry’s first unified, modern, hybrid analytics and data lakehouse platform.

We believe it is the first solution to architecturally optimize and leverage three key advancements simultaneously which no one else in the industry has done.

1.     Optimize for a Kubernetes based Spark environment for on-premises deployment providing the cloud-native elasticity and agility customers want

2.     Handle the diversity of data types from files, tables, streams, and objects in one consistent platform to avoid silos and make data engineering easier

3.     Embrace the edge by enabling a data platform environment which can span from edge to hybrid cloud

Instead of requiring all of your data to live in a public cloud, HPE Ezmeral Unified Analytics is optimized for on-premises and hybrid deployments, and uses open source software to ensure as-needed data portability. We designed our solution with the flexibility and scale to accommodate enterprises’ large data sets, or lakehouses, so customers have the elasticity they need for advanced analytics, everywhere.

ust a few key advantages of HPE Ezmeral Unified Analytics include:

  •  Dramatic performance acceleration: Together NVIDIA RAPIDS Accelerator for Apache Spark and HPE Ezmeral can accelerate Spark data prep, model training, and visualization by up to 29x, allowing data scientists and engineers to build, develop, and deploy at scale analytics solutions into production faster.
  • Next-generation architecture: We have built on Kubernetes and added value through an orchestration plane to make it easy to get the scale-out elasticity customers want. Our multi-tenant Kubernetes environment supports a compute-storage separation cloud model, providing the combined performance and elasticity required for advanced analytics, while enabling users to create unified real-time and batch analytics lakehouses with Delta Lake integration.
  • Optimized for data analytics: Enterprises can create a unified data repository for use by data scientists, developers, and analysts, including usage and sharing controls, creating the foundation for a silo-free digital transformation that scales with the business as it grows, and reaches new data sources. Support for NVIDIA Multi-Instance GPU technology enables enterprises to support a variety of workload requirements and maximize efficiency with up to seven instances per GPU.
  • Enhanced collaboration: Integrated workflows from analytics to ML/AI span hybrid clouds and edge locations, including native open-source integrations with Airflow, ML Flow, and Kubeflow technologies to help data science, data engineering, and data analytics teams collaborate and deploy models faster.
  • Choice and no vendor lock-in: On-premises Apache Spark workloads offer the freedom to choose deployment environments, tools, and partners needed to innovate faster
  • “Today’s news provides the market with more choice in deploying their modern analytics initiatives with a hybrid-native solution, enabling faster access to data, edge to cloud,” said Carl Olofson, Research Vice President, IDC. “HPE Ezmeral is advancing the data analytics market with continued innovations that fill a gap in the market for an on-premises unified analytics platform, helping enterprises unlock insights to outperform the competition.”

HPE Ezmeral Data Fabric Object Store

Our second disruptive new solution is the HPE Ezmeral Data Fabric Object Store: the industry’s first Data Fabric to combine S3-native object store, files, streams and databases in one scalable data platform that spans edge-to-cloud. Available on bare metal and Kubernetes-native deployments, HPE Ezmeral Data Fabric Object Store provides a global view of an enterprise’s dispersed data assets and unified access to all data within a cloud-native model, securely accessible to the most demanding data engineering, data analytics, and data science applications. Designed with native S3 API, and optimized for advanced analytics, HPE Ezmeral Data Fabric Object Store enables customers to orchestrate both apps and data in a single control plane, while delivering the best price for outstanding performance.

We are proud of the innovation that has resulted in what we believe is an industry first: A consistent data platform which is able to handle a diversity of data types, is optimized for analytics, and is able to span from edge to cloud.

Several key features include: 

  • Optimized performance for analytics: Designed for scalable object stores, HPE Ezmeral Object Store is the industry’s only solution that supports file, streams, database, and now object data types within a common persistent store, optimized for best performance across edge-to-cloud analytics workloads.

  • Globally synchronized edge-to cloud data: Clusters and data are orchestrated together to support dispersed edge operations, and a single Global Namespace provides simplified access to edge-to-cloud topologies from any application or interface. While data can be mirrored, snapshotted, and replicated, advanced security and policies ensure the right people and applications have access to the right data, when they need it.

  • Continuous scaling: Enterprises can grow as needed by adding nodes and configuring policies for data persistence while the data store handles the rest.

  • Performance and cost balance: Adapting to small or large objects, auto-tiering policies automatically move data from high-performance storage to low-cost storage.  

For more information contact [email protected]


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