Version 2 of ScaleOut Software’s Digital Twin Service Yields Support for Application Development

ScaleOut Software, developers of scalable, highly available, in-memory computing and streaming analytics technology software, is debuting Version 2 of the ScaleOut Digital Twin Streaming Service for implementing streaming analytics in live systems via the digital twin model. The latest launch builds from the Version 1 iteration of the program, consisting of machine learning integration into digital twins with Microsoft’s ML.NET library, intuitive business rules to create digital twin models, and Microsoft’s Azure Digital Twins integration.

“We are pleased to release the second major version of our digital twin streaming service with exciting new features for developers,” said Dr. William Bain, CEO and founder of ScaleOut Software. “Digital twins have the potential to significantly boost situational awareness for a wide range of mission-critical applications. We believe this release will further accelerate adoption of digital twins in applications that track live systems with many data sources.”

ScaleOut emphasizes a need to meet the modern requirements of real-world applications, and considers digital twins to be the answer. With Version 2, digital twins are equipped with extensive support for tracking data sources for a range of live, mission-critical use cases, such as healthcare IT, disaster response, and physical security systems, according to the vendor.

C#-based digital twins receive greater support with Version 2, utilizing .NET 6 for on-premises deployment within Windows and Linux operating systems of C# digital twin models; previously, using the .NET Framework was limited to Windows. Java or JavaScript for digital twin model builds is also available in the latest version, employing a built-in rules engine.

Version 2 of the ScaleOut Digital Twin Streaming Service includes integrated timers that, when created by developers, can initiate code execution for enhanced missing or delayed messages detection from data sources. Alerting developers of signal alerts, this capability is critical to live applications responsible for locating failed or unreliable devices—including smoke detectors and security system sensors.

Unlike Version 1’s limitation in automatic digital twins creation during messaging from data sources, Version 2 provides increased control to users through creating and triggering digital twins via file-based data. This feature also supports supplying an optional .csv file to the UI when a model is deployed for that purpose, according to the vendor.

For more information about Version 2, please visit