Meet Edge Computing: The New Trend In Technology

Edge Computing: According to Gartner, a research institute dedicated to the IT sector, investments in solutions that consider cloud computing will double in the coming years compared to solutions that do not use the cloud.

In this dynamic and novelty scenario, a new industry trend emerges: Edge Computing, or in literal translation, edge computing. In general terms, it is an unfolding of advances in the cloud and new technologies.

Edge Computing presupposes the decentralization of data storage and processing, which happens closer to where the information is generated and used. It is a phenomenon that occurs thanks to the emergence of new technologies such as the Internet of Things and Artificial Intelligence.

The fact that multi connections are on different devices, regardless of location, ends up pulverizing this data storage and processing network – which, with so many connected devices, no longer needs to be tied only to the cloud.

The democratization of access and production of this information, combined with a growing volume of data deposited on the network, creates a troubled environment in which depending on just a single cloud can be a factor that makes the transmission of information difficult.

Data ownership and governance must be treated as a public good in this new era. The fact that they are constantly connected to a central cloud can even be a limiting factor in access from other devices. The cloud can limit machine learning.

Unlike Cloud Computing, which operates at the internet level, the logic of Edge Computing is at the service provider level – LAN/WAN. It involves real-time data processing, analysis, loading, filtering, and optimization. Generally, it provides faster assessments and localized actions for IT modernization in companies.

Edge Computing, IoT, And Predictions For The Future

According to some experts, the sequence of the so-called 4th Industrial Revolution should culminate in the disappearance of the cloud. This is because, given the independence of other devices in terms of consumption, storage, and exchange of information, the cloud is no longer a fundamental part of the process.

Going back in time, the connection was once based on centralized structures – mainframes. The operations within the companies relied on large server stations and even cooling machines to coordinate the services of the IT sector.

These large centrals gave way to a new, more decentralized moment that only connected clients and servers through clouds. If this ebb and flow are repeated, the trend is for the cloud – now in the role of a centralizing network – to give way to even more dynamic forms, such as tools linked to the IoT.

In recent decades, moments like this, pre-disruption, have become commonplace in the internet’s escalation. In this scenario, what must be taken into account is the ability to adapt services and products to this new wave that, while presupposing interconnectivity, defends greater decentralization.

From Big Data to Machine Learning, the process of handling and manipulating data has evolved. And information security has become even more paramount.

And now, with the advent of Edge Computing, the idea is that the closer the information is analyzed to the point at which it was collected, the better for the system. This ends with the wireless phone that often compromises the company’s work in transmitting the information.

Cloud On Board: Building The Future With Google

Cloud On Board is an accessible technical introduction to Google Cloud Platform (GCP) training for developers and anyone new to the cloud. Cloud experts come together once again to discuss, learn and share information about the world of cloud technology.

Google’s engineering team meets for presentations and technical demonstrations, exploring cloud concepts such as virtual machines and Kubernetes or data tools such as Cloud Daturic, Dataflow, Tensor Flow, and Big Query.

There are two different training pieces with varying content sessions – Core Infrastructure and Big Data & Machine Learning. The participant must choose based on their prior knowledge of the subject.

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