An edge data fabric acts as both the plumbing and translator for data moving on and off different platforms deployed at the edge.
To generate richer and more timely insights, enterprises are using increasing amounts of data. Expect that trend to continue. An IDC model projects that the global datasphere will roughly triple in size by 2025.
But this trend isn’t new. After all, the term Big Data has been with us for quite a while. What’s different is where the data will emanate from and how fluid it will be. In other words, mobile and IoT – the edge – will drive data creation.
Further, the processing and analysis will happen at various points from on device, at the gateways, and across the cloud. Perhaps a better term would be Fluid Distributed Data instead of Big Data?
Regardless, more data ultimately translates to more viable business opportunities – particularly given that this new data is generated at the point of action from humans and machines.
To take full advantage of the growing amounts of data available to them, enterprises need a way to manage it more efficiently across platforms, from the edge to the cloud and back. They need to process, store and optimize different types of data that’s coming from different sources with different levels of cleanliness and validity. They need to connect this data to internal applications and apply business process logic, increasingly aided by artificial intelligence and machine learning models.
It’s a big challenge. One solution enterprises are pursuing now is the adoption of adata fabric.And, as data volumes continue to grow at the network’s edge, that solution will evolve further into what will more commonly be referred to as an edge data fabric.
Data that is distributed across different areas can be accessed easily and transparently – in real time in a unifying data layer, under the same management – through a data fabric. The data fabric itself enables operators to move and access data across different deployment platforms, data processes, geographical locations and structural approaches.
Essentially, a data fabric acts as both the plumbing and translator for data moving onto and off different platforms – including data centers, the public cloud, private clouds and the many types of gateways and devices operating at the edge.
Edge computing provides a unique set of challenges for data being generated and processed outside the network core. The devices themselves operating at the edge are getting more complex.
Smart devices like networked PLCs manage solenoids that, in turn, control process flows in a chemical plant, pressure sensors that determine the weight and active RFID tags to determine the location of a cargo container. The vast majority of the processing used to take place in the data center, but that has shifted to the point where a larger portion of the processing takes place in the cloud. In both cases, the processing happens on one side of a gateway.
The data center was fixed, not virtual, but the cloud is fluid. If you consider the definition of cloud, you can see why a data fabric would be needed in it. Cloud is about fluidity and removing locality, but, like the data center, it’s about processing data associated with applications.
We may not care where the Salesforce cloud or Oracle cloud or any other cloud is actually located but we do care that my data must transit between various clouds and persist in each of them for use in different operations.
Because of all that complexity, organizations have to determine which pieces of the processing are done at which level. There’s an application for each, and for each application there’s a manipulation. And for each manipulation, there’s processing of data and memory management.
The point of a data fabric is to handle all the complexity.
