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By bringing computation closer to the data source, edge-based deployments reduce latency, enhance real-time capabilities, and optimize network bandwidth. Data Overload and Storage Limitations As IoT and especially industrial IoT -based devices proliferate, the volume of data generated at the edge has skyrocketed.
This enables customers to serve content to their end users with low latency, giving them the best application experience. In 2008, AWS opened a point of presence (PoP) in Hong Kong to enable customers to serve content to their end users with low latency. Since then, AWS has added two more PoPs in Hong Kong, the latest in 2016.
Volt’s architecture supports energy management applications with its low-latency, high-availability data processing, making it ideal for tracking and optimizing real-time energy usage across industrial sites.
Increased efficiency Leveraging advanced technologies like automation, IoT, AI, and edge computing , intelligent manufacturing streamlines production processes and eliminates inefficiencies, leading to a more profitable operation. Let’s take a look.
Some of the most common use cases for real-time data platforms include business support systems, fraud prevention, hyper-personalization, and Internet of Things (IoT) applications (more on this in a bit). One common problem for real-time data platforms is latency, particularly at scale.
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