Today ScaleOut Software announces the release of its ground-breaking cloud service for streaming analytics using the real-time digital twin model. It’s called the ScaleOut Digital Twin Streaming Service™, and it’s now available for production use. Sign up to use the service here.

A major challenge for stream-processing applications that track numerous data sources in real time is to analyze telemetry relevant to each specific data source and combine this with dynamic, contextual information about the data source to enable immediate action when necessary. For example, heart-rate telemetry from a smart watch cannot be effectively evaluated in isolation. Instead, it needs to be combined with knowledge of each person’s age, health, medications, and activity to determine when an alert should be generated.

A second and equally daunting challenge for live systems is to maintain real-time situational awareness about the state of all data sources so that strategic responses can be implemented, especially when a rapid sequence of events is unfolding. Whether it’s a rental car fleet with 100K vehicles on the road or a power grid with 40K nodes subject to outages, system managers need to quickly identify the scope of emerging problems and rapidly focus resources where most needed.

Traditional platforms for streaming analytics attempt to look at the entire telemetry pipeline using techniques such as SQL query to uncover and act on patterns of interest. But this approach is complex and leads to superficial analysis in real time, forcing telemetry to be logged into a data lake for later analysis using Spark or other tools. How do you trigger an alert to the wearer of a smart watch at the exact moment that the combination of telemetry fluctuations and knowledge about the individual’s health indicate that an alert is needed?

The key to creating straightforward stream-processing applications that can deal with these challenges lies in a software concept called the “real-time digital twin model.” Borrowed from its use in the field of product life-cycle management, real-time digital twins host application code that analyzes incoming telemetry (event messages) from each individual data source and maintains dynamically evolving information about the data source. This approach refactors and simplifies application code (which can be written in standard Java, C#, or JavaScript) to just focus on a single data source, introspect deeply, and better predict important events.

The following diagram illustrates how the ScaleOut Digital Twin Streaming Service hosts real-time digital twins that receive telemetry from individual data sources and send responses, including commands and alerts:

Because real-time digital twins maintain and dynamically update key information about each data source, aggregate analytics — essentially, continuous queries —  can continuously look for patterns in this curated data instead of in just the raw telemetry. This enables immediate, focused insights that enhance situational awareness. For example, the streaming service can generate a bar chart every few seconds to aggregate and highlight alerts by region generated by examining properties of real-time digital twins for thousands of data sources:

The ScaleOut Digital Twin Streaming Service plugs into popular event hubs, such as Azure IoT Hub, AWS IoT Core, and Kafka, to extract event messages and forward them to real-time digital twin instances, one for each data source. It then triggers application code to process the messages and gives it immediate access to memory-based contextual information for the data source. Application code can generate alerts, command devices, update the contextual information, and read or update databases as needed. This code can be thought of as similar to a serverless function with the major distinction that it is automatically supplied contextual information and does not have to maintain it in an external data store.

This highly scalable cloud service is designed to simultaneously and cost-effectively track telemetry from millions of data sources and provide real-time feedback in milliseconds while simultaneously performing continuous, aggregate analytics every few seconds. A powerful UI enables fast deployment of real-time digital twin models created using the ScaleOut Digital Twin Builder™ software toolkit. The UI lets users build graphical widgets which create and chart aggregate statistics. Under the floor, a powerful in-memory data grid with an integrated compute engine transparently ensures fast, predictable performance.

Given the current COVID-19 crisis, here’s a use case in which the streaming service can assist in prioritizing the distribution of critical medical supplies to the nation’s hospitals.  Hospitals distributed across the United States can send status updates to the cloud service regarding their shortfall of supplies such as ventilators and personal protective equipment. Within milliseconds, a dedicated real-time digital twin instance for each hospital can analyze incoming messages to track and evaluate the need for supplies, determine the hospital’s overall shortfall, and assess the urgency for immediate action, as depicted below:

The streaming service can then simultaneously analyze these results across the population of digital twin instances to determine in seconds which regions are exhibiting the most critical shortfall. This alerts logistics managers, who can then query the digital twins to identify specific hospitals and implement a strategic response:

The real-time digital twin approach creates a breakthrough for application developers that both simplifies application development and enhances introspection. It’s ideal for a wide range of applications, including real-time intelligent monitoring (the example above), Industrial Internet of Things (IIoT), logistics, security and disaster recovery, e-commerce recommendations, financial services, and much more. The ScaleOut Digital Twin Streaming Service is available now. We invite interested users to contact us here to learn more.