Making the Edge and Cloud Work for Enterprise Apps

by Brad Johnson, on Jun 26, 2018 1:30:56 PM

There’s a world of data being created that never reaches it to the cloud. Some edge data is collected and forwarded over local networks for cloud native apps. SWIM.AI CTO, Simon Crosby, explains in the most recent L8istSh9y Podcast, “the enterprise as we know it is drowning in vast amounts of data. Most of it hits the floor, and yet everyone believes that there are vast amounts of insights hidden in there which could really transform productivity or safety.” Despite the potential of valuable insights which are lost to data that is never analyzed, Crosby states that the problem is “no one knows what to do [with their edge data].” It’s not enough to use edge computing technologies to get data into the cloud. At the same time, edge computing will not replace cloud-native solutions. Instead, to achieve full value from edge data, decision makers must focus on ways edge computing can complement cloud-native applications.

Why Enterprise Apps Need the Edge and the Cloud

Edge computing may seem like it’s only an IoT problem, but edge data challenges are myriad across Industry and  in the Enterprise. For example, Oil & Gas companies are investing billions of dollars in machinery and sensors which pump out tons of data. On the consumer side, widgets connected into your home generate more data than ever before. The fundamental problem here is how to process and utilize edge data locally, in tandem with cloud-native applications. SWIM is here to solve that problem.

SWIM creates digital twins of real-world devices and processes. Every entity in the real world has a virtual representation, or digital twin, which can be accessed programmatically. Each digital twin consumes data from the real world sibling and is responsible for analyzing it, knowing its current state, and projecting its future values if they are relevant to the current problem. This can have significant real-world implications. An an example, Simon Crosby highlights the SWIM.AI Smart City application where individual intersections are represented as “entities which learn to predict their own future by training their own models. For example, an intersection is about 50 sensors [in the SWIM.AI Smart City Application], and the goal is to predict the future value of those sensors.” SWIM bridges the edge and cloud, aggregating available data streams from local and cloud sources to derive predictions and make them available via APIs.

Listen to the full podcast here:

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Learn how SWIM EDX bridges the edge and the cloud for IoT applications and other distributed applications.

Topics:Machine LearningStateful ApplicationsSWIM SoftwareIndustrial IOTTraffic ManagementEdge AnalyticsDigital TwinSWIM AIEdge ComputingSWIM EDXPodcast