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Many enterprises have their high-performance databases supported by traditional infrastructure. Managing these segregated workloads results in burdensome manual processes that can expose your database to costly human error. In addition to high maintenance costs, database sprawl, and increased security risk, such siloed operations on traditional infrastructure result in slow provisioning, inflexibility, poor quality insights, and expensive workarounds for disaster recovery.
While the costs and risks of an on-premises cloud migration are typically lower than those of a public cloud migration, most organizations are looking for a hybrid model that utilizes on-premises cloud, multiple public clouds, and edge computing to deliver the right mix of decision-making effectiveness, agility, security, and cost.
With HPE guiding you through the process, you can achieve your right mix to unlock value in your data wherever it resides, while reducing technological and financial risks. Even though most organizations use an average of five cloud solutions, a majority of core business applications run on expensive, legacy platforms. But with one accountable migration partner with thousands of successful enterprise-wide hybrid and multi-cloud adoption engagements—including HPE GreenLake on-premises cloud with easier, fewer, and less risky steps—you can easily reap the full value of transformation that will move your business forward.
The HPE ConvergedSystem 300 for Microsoft Analytics Platform is designed by Hewlett Packard Enterprise and Microsoft® to deliver high performance and throughput, with up to 12.6 PB of capacity. Scalability and massively parallel processing (MPP) support high-volume data demands and the ability to analyze all data types with Microsoft’s integrated query tool, PolyBase. PolyBase ties the worlds of relational and non-relational data together, so they operate seamlessly in this single data warehouse appliance. End-to-end integration shortens deployment from months to days and eases ongoing operations. Hewlett Packard Enterprise and Microsoft have come together to help enterprises more easily implement data warehouse solutions by designing and delivering a complete solution built on a proven architecture, with a fine-tuned configuration. The collaborative support model brings faster resolution to issues if they arise.
The HPE Ezmeral Unified Analytics solution modernizes legacy data and applications to optimize data-intensive workloads from edge to cloud to deliver the scale and elasticity required for advanced analytics.
HPE InfoSight for Servers combines the machine learning and predictive analytics of HPE InfoSight with the health and performance monitoring of Active Health System (AHS) and HPE Integrated Lights Out (iLO) to optimize performance and predict and prevent problems. HPE InfoSight for Servers nearly eliminates wasted time and headaches by transforming how infrastructure is managed and supported. AHS is like a “flight recorder” for your server that provides continuous, proactive health monitoring and recording thousands of system parameters and diagnostic telemetry data 24x7 on the server. HPE InfoSight for Servers analyzes the telemetry data from AHS to derive insights from the behaviors of the install base to provide recommendations to resolve problems and improve performance. iLO Amplifier Pack functions as an on-premise aggregator and transmitter for HPE InfoSight for servers.
Organizations in every industry are looking to leverage AI and ML to harness the power of their data and deliver business innovation through data science. But even when they achieve some measure of success with machine learning pilot programs, many organizations face challenges when they seek to scale these programs to production: security concerns, legacy hardware, siloed data and workflows, inefficient processes, and daunting costs.
To deliver the value of ML and data science to your enterprise, the HPE GreenLake platform delivers an enterprise-grade ML cloud service that enables developers and data scientists to rapidly build, train, and deploy ML models—from pilot to production, at any scale. This solution comprises an optimized hardware stack and is powered by HPE Ezmeral ML Ops. It provides data scientists with self-service access to a sandbox environment for prototyping and testing, to eliminate IT provisioning delays, ensure repeatability, and accelerate time-to-value.