Software

How does my organization more efficiently and effectively analyze data in my Hadoop cluster environment? HPE Vertica SQL on Hadoop offers the fastest and most enterprise-ready way to perform SQL queries on your Hadoop data. We’ve leveraged our years of experience in the big data analytics marketplace and opened up our platform to use the full power of the Hadoop cluster. By offering an open, fast and enterprise-ready implementation of SQL on Hadoop, users can perform analytics regardless of the format of data or Hadoop distribution used.

Click here for more details on how HPE Vertica SQL on Hadoop handles your mission-critical analytics projects by merging the best of our analytics platform with the best that Hadoop can offer.

Do you need to scale your machine learning (ML) and data pipelines when processing large amounts of structured and unstructured data?

HPE Machine Learning Data Management Software delivers a flexible data layer platform that automates complex ML and data pipelines, while providing data versioning and lineage for reproducibility. Increase the performance of your pipelines with autoscaling and parallel processing built on Kubernetes for resource orchestration. Standard object stores, deduplication, and pipelines are automatically triggered when data is modified, thus saving your engineers time and resources. This platform features immutable data lineage with data versioning of any data type to provide traceable results, allowing any outcome to be reproduced.

Do your servers need increased performance of in-memory computing to keep pace with unprecedented growth in data volume and velocity?

HPE Scalable Compute Software is a flexible solution that aggregates the memory, processors, and I/O of multiple physical servers to create a single software-defined server that can reliably power your business-critical applications.

By running a proprietary, bare-metal hypervisor on each machine, a single software-defined server can virtualize the physical resources of every server to improve performance. Each node can be hot-swapped without impacting the operation of other nodes to provide increased server uptime.

By utilizing 1-, 2-, or 4-socket commodity servers and standard Ethernet networks, HPE Scalable Compute Software dramatically increases availability and provides the ideal solution to verify your data does not outgrow your infrastructure or your budget.

Is your channel partner or telco enterprise struggling to manage multiple mobile private networks? Athonet Dashboard is a central Private Network Management platform that features a multi-tenant software management system which provides segmented resources and role-based access for enhanced security. Allow system admins and operators to handle multiple channel partners and customers with user-specific roles, access, and resources. Simplify operations with central provisioning (devices/USIMs), configuration, and monitoring with a single pane of glass. Utilize Athonet Dashboard to manage multiple mobile private networks for efficient provisioning of devices, profiles, and associated quality of service (QoS) with monitoring and analysis of traffic data in real-time.

Does your enterprise/vertical industry need to easily deploy and scale voice services over private mobile networks? Athonet IP Multimedia Subsystem (IMS) is a market proven, simple, intuitive, robust, compact, and comprehensive solution that unites the power of VoLTE and VoNR high-quality voice and video services to provide feature-packed private 4G EPC and 5G Core cellular networks. By enabling various types of multimedia services for end-users using common Internet-based protocols, Athonet IMS deploys a fully-local IMS to address the increasing demand in VoLTE and communications services. Developed for 3GPP standards and 5G-ready for VoNR, Athonet IMS can be integrated with a PBX and other SIP networks for voice, video, and SMS services as well as EPC (Evolved Packet Core) and 5G Core. Utilize multi-access, multi-service, multi-device, and multi-networks within a flexible IT context for simplified operation and adaptable maintenance.

Do you need a 4G/5G Mobile Private Network to power mission critical applications with impressive performance and a small footprint? Athonet Mobile Core Software provides a proven and robust mobile private network platform that allows deployment of 4G EPC and 5G Core networks with Wi-Fi-like simplicity to install and operate. With network slicing and voice services using VoNR with IMS add-on, you can confidently support current and advanced use cases with above 25 Gbps throughput and flexible scalability for a large number of connections on standard hardware. Easily manage this flexible solution deployed on on-premises server(s) or private cloud.

Are your model development and MLOps teams spending more time setting up and managing ML infrastructure, rather than building and deploying models to production?

HPE Machine Learning Development Environment as a managed service is a fully managed MLOps platform that allows model developers and researchers to focus on building better models faster by reducing complexity and removing the need to write boilerplate code associated with managing ML infrastructure. It easily integrates with ML frameworks and tools, and supports customers bringing their own AWS or GCP cloud environments.

Our platform also makes it easy for IT and MLOps teams to setup and share AI infrastructure to improve collaboration and productivity for ML teams, while reducing costs.

Train models faster, build more accurate models, efficiently manage and share AI infrastructure, and track and reproduce experiments easily with HPE Machine Learning Development Environment as a managed service.

Are you concerned about data privacy or regulations when moving data for your machine learning (ML)? Is your data distributed and preventing you from collaboration for improved insights where they are most needed? HPE Swarm Learning provides decentralized privacy-preserving, edge ML at the data source. The blockchain network provides the ability to collaboratively share the learnings of the models with participating HPE Swarm Learning nodes for insights at the data source, tremendously enhancing data privacy and improving insights.

HPE Swarm Learning extends federated learning and obviates the need for a central server. A decentralized, privacy-preserving ML framework utilizes the computing power at, or near, the distributed data sources to run the ML algorithms that train the models. Training the model occurs at the edge where data is most recent, where accurate, and data-driven decisions are necessary.