Infrastructure – Should data be crunched at the centre or at the edge?

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Infrastructure – Should data be crunched at the centre or at the edge?

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AWS represents a centralised model where all the data are collected and crunched in a few places, namely big data centres.
Similarly, for every warehouse-sized data centre, there will be an endless network of cables and connections, collecting data from every nook and cranny of the world.
Now, more and more data flow into the big computing factories operated by AWS , but also its main competitors, Microsoft Azure, Alibaba Cloud and Google Cloud.
What Mr Vass also prefers not to say, is that AWS and other big cloud-computing providers are striving mightily to deepen this centralisation.
And once a firm’s important data are in the cloud, it will move more of its business applications to the computing skies, generating ever more revenue for cloud-computing providers.
Cloud providers also offer an increasingly rich palette of services which allow customers to mine their data for insights.
Happily, a counter-movement has already started—toward the computing “edge”, where data are generated.
It is not just servers in big data centres that are getting more powerful, but also smaller local centres and connected devices themselves, thus allowing data to be analysed closer to the source.
Applications such as self-driving cars need very fast-reacting connections and cannot afford the risk of being disconnected, so computing needs to happen in nearby data centres or even in the car itself.
And in some cases the data flows are simply too large to be sent to the cloud, as with the traffic lights in Las Vegas, which together generate 60 terabytes a day (a tenth of the amount Facebook collects in a day).
And Simon Crosby, the chief technologist at SWIM.AI , while admitting that his firm’s approach “does not apply everywhere”, argues that too much data are generated at the edge to send to the cloud, and there will never be enough data scientists to help train all the models centrally

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