Here’s your chance to learn about an exciting and important innovation for stream-processing: digital twins. This new approach opens the door to much deeper introspection on streaming data than previously possible, while at the same time simplifying your application’s design. Unlike traditional stream-processing, which focuses on just extracting information from incoming data streams, digital twins let you create dynamic models of your real-world data sources, enabling you to make better predictions and create more effective feedback within live systems.
This webcast takes you step-by-step through the creation, deployment, and use of digital twin models. Using several examples, it shows how their object-oriented formulation in Java and C# simplifies application design and enables fast message processing, while easily handling large workloads with thousands of data sources. We will explore how digital twins let you efficiently track the dynamic state of your data sources so that you can better analyze their behavior. We also will look at another amazing capability they unleash: real-time, data-parallel analytics that detect aggregate trends and feed them back into your stream-processing algorithms.
In short, this webcast will show you how digital twins have now moved beyond product lifecycle management (PLM) to help you meet real-time operational goals, such as maximizing uptime, predicting imminent failures, and optimizing performance. The next generation of stream-processing has arrived.
