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Articles for the keywords: Analytics
10 May 2017 NetApp Showcases Hybrid Cloud Data Management Solutions for SAP HANA and Applications at 2017 SAPPHIRE NOW [63338]
NetApp, May 10th 2017

NetApp Innovation Helps Enterprises Accelerate SAP Projects, Optimize Investment

NetApp, an SAP partner for more than 17 years, will showcase collaborative solutions for the hybrid cloud that speed SAP implementations, improve operational efficiency, and reduce risk at 2017 SAPPHIRE NOW. The event will take place May 16-18, 2017, at the Orange County Convention Center in Orlando, Florida.

With more than 26,000 joint customers, NetApp and SAP have extensive experience in solution development for hybrid cloud, converged infrastructure, CRM, and predictive analytics. The companies bring together their expertise on projects ranging from co-innovation labs to supporting the world's largest data warehouse.
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10 May 2017 HPE Accelerates Real-Time Insights for Deep Learning [63316]
HPE, May 10th 2017

Hewlett Packard Enterprise announced a comprehensive set of computing innovations to accelerate Deep Learning analytics and insights across all organizations with innovations spanning systems design, partner ecosystem collaboration, and expertise including flexible consumption models from HPE Pointnext Services.

Advanced artificial intelligence (AI) techniques, such as Deep Learning, are growing in popularity across various sectors including financial services, life sciences, manufacturing, energy, government and retail. HPE has a strong track record of delivering comprehensive, workload optimized compute solutions for all AI and Deep Learning with its purpose-built HPE Apollo portfolio that maximizes performance, scale and efficiency. With the latest innovations specifically targeted to Deep Learning, leveraging capabilities from the recent SGI acquisition, HPE now offers greater choice for larger scale, dense GPU environments and addresses key gaps in technology integration and expertise with integrated solutions and services offerings.
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09 May 2017 Big Data Use Case g Ticketmaster: Cloud Migration Experiences [63235]
insideBIGDATA, May 9th 2017

"The insideBIGDATA technology use case guide - Ticketmaster: Using the Cloud Capitalizing on Performance, Analytics, and Data to Deliver Insights provides an in-depth look at a high-profile cloud migration use case. One of the most widely discussed topics in IT today is moving workloads to cloud. The process of deciding whether or when to migrate to the cloud can be daunting. Further, finding the right technology fit for your enterprise objectives can be challenging with a cloud solution ecosystem filled with alternatives. To make the cloud adoption process more straightforward, this white paper provided a number of areas for consideration when evaluating a cloud platform..."
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09 May 2017 Report: New Logistics Pave Road For Machine Data Analytics [63234]
SmartDataCollective, May 9th 2017

"Machine data analytics is the process of using big data from a variety of devices to solve complex, real-world challenges. Machine data analytics can aggregate data from smartphones, websites, desktop devices and Internet servers.

How Brands Are Turning to Machine Data Analytics

Machine data is expected to transform the service models of countless businesses across the world. Machine data analytics can be used in a variety of other applications, including:..."
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09 May 2017 Dell Technologies Simplifies IoT for Customers with New IoT Products and Partnerships [63312]
Dell, May 9th 2017

Dell Technologies announces new Internet of Things (IoT) products and partnerships to help customers take the complexity out of their IoT deployments and more quickly realize Digital Transformation.

According to a recent Gartner report, there will be 20.4 billion connected things in use globally by 2020. Companies are looking for faster, real-time analysis of the massive amount of data produced by all of these "things" on their networks. For some, it's too expensive to move all the data from the edge of the network near the devices to the data center. Computing at the edge helps determine which data sets are interesting, relevant and need to be sent back to the data center or the cloud for further analytics and longer term storage, saving bandwidth and reducing costs and security concerns.
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