Remove Big Data Remove Data Center Remove IoT Remove Microservices
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Five Trends for 2019

Hu's Place - HitachiVantara

While the majority of the IoT products, services, and platforms are supported by cloud-computing platforms, the increasing high volume of data, low latency and QoS requirements are driving the need for mobile cloud computing where more of the data processing is done on the edge.

Trends 86
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Cloud Migration Fundamentals

Linux Academy

Cloud computing is not just about having virtual servers in an off-premises data center. When he saw the costs of the infrastructure, networking, and other cloud resources he asked, “Why would we ever do anything in our own data center again?”. Last but not least is cloud optimization.

Cloud 60
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Industry 4.0: What Could Possibly Go Wrong?

Kentik

However, in this post and the next, I’d like to take a closer look at the unique challenges of managing network performance inside the new generation of hyperscale data centers supporting the delivery of cloud-scale applications. applications. Industry 4.0 Industry 4.0 An Industry 4.0

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5 Essential Technologies to get your Distributed Enterprise Future Ready

Trigent

With upcoming 5G rollouts, massive IoT networks, mmWave, and network slicing requirements, their cloud and edge capabilities will be of an entirely different scale. When Ducati, a global automotive giant, undertook a data center modernization project, they expected gains, but that was not how it turned out.

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Understanding the Scope of AIOps and Its Role in Hyperautomation

Trigent

74% of CIOs were reportedly using cloud-native technologies, including microservices, containers, and Kubernetes, and 61% said these environments changed every minute or less. Artificial intelligence, machine learning, big data have all been spoken about extensively and form the very backbone of AIOps. What is AIOps?

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Understanding the Scope of AIOps and Its Role in Hyperautomation

Trigent

74% of CIOs were reportedly using cloud-native technologies, including microservices, containers, and Kubernetes, and 61% said these environments changed every minute or less. Artificial intelligence, machine learning, big data have all been spoken about extensively and form the very backbone of AIOps. What is AIOps?

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Predictive Maintenance: Employing IIoT and Machine Learning to Prevent Equipment Failures

Altexsoft

But such improvements require significant investments in IT infrastructure and expertise — namely, in industrial IoT (IIOT) sensors, analytics software with machine learning capabilities, services of data scientists and IT specialists, staff training. Splunk , an industrial analytical tool already integrated with leading IoT platforms.