ScaleOut Digital Twin Streaming Service™

Digital Twin Streaming Service
  • Easily build and deploy digital twins for streaming analytics; seamlessly integrate with Azure Digital Twins
  • Connect to many data sources with Azure & AWS IoT hubs, Kafka, and more
  • Analyze telemetry using no-code machine learning with ML.NET or a rules engine
  • Maximize situational awareness with live, aggregate analytics
Digital Twin Streaming Service

Breakthrough Cloud Service for Real-Time Analytics

Introducing a breakthrough cloud service that simultaneously tracks telemetry from millions of data sources with “real-time” digital twins — enabling immediate, deep introspection with state-tracking and highly targeted, real-time feedback for thousands of devices. A powerful UI simplifies deployment and displays aggregate analytics in real time to maximize situational awareness. Ideal for a wide range of applications, including the Internet of Things (IoT), real-time intelligent monitoring, logistics, and financial services. Simplified pricing makes getting started fast and easy. Combined with the ScaleOut Digital Twin Builder software toolkit and the ScaleOut Model Development Tool™ for ML.NET machine learning and rules-based development, the ScaleOut Digital Twin Streaming Service enables the next generation in stream processing.

Now seamlessly integrate with Azure Digital Twins to unlock new use cases for real-time analytics. Learn more about the ScaleOut Azure Digital Twins Integration.

A web-based UI simplifies the deployment and management of real-time digital twin models. It also enables fast, easy creation of real-time, aggregate analytics that combine the state of all real-time digital twins of a given type and provide immediate, graphical feedback that helps users maximize situational awareness.


ScaleOut’s cloud service runs as an in-memory computing platform based on ScaleOut StreamServer®. This highly scalable platform automatically directs incoming telemetry to real-time digital twins and responds back to devices within 1-3 milliseconds while generating aggregate statistics every 5 seconds.


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The Power of Real-Time Digital Twins

A Breakthrough for Real-Time Streaming Analytics

Traditional stream-processing and complex event-processing systems focus on extracting patterns from incoming telemetry, but they can’t track dynamic information about individual data sources. This makes it much more difficult to fully analyze what incoming telemetry is saying. For example, an IoT predictive analytics application attempting to avoid an impending failure in a population of medical freezers must look at more than just trends in temperature readings. It needs to evaluate these readings in the context of each freezer’s operational history, recent maintenance, and current state to get a complete picture of the freezer’s actual condition.

That’s where the power of real-time digital twins comes in. While digital twin models have been used for several years in product life cycle management, their application to stateful stream-processing has only now been made possible by advances in scalable, in-memory computing. Unlike traditional streaming pipelines, like Apache Storm and Flink, real-time digital twins offer a simple, intuitive technique for organizing important, dynamically evolving, state information about each individual data source and using that information to enhance the real-time analysis of incoming telemetry. This enables deeper introspection than previously possible and leads to significantly more effective feedback — all within milliseconds.

Real-time digital twins also provide a powerful means for deploying machine learning (ML) capabilities that track incoming telemetry and look for anomalies that require alerting. By running within digital twins, ML algorithms can be tailored for each type of device and its parameters, and they can run independently and simultaneously for thousands of data sources.

Equally important, the state-tracking provided by real-time digital twins allows immediate, aggregate analytics to be performed every few seconds. Instead of deferring aggregate analytics to batch processing on Spark, real-time digital twins enable important patterns and trends to be quickly spotted, analyzed, and handled. This dramatically improves situational awareness. For example, if a regional power outage takes out a group of medical freezers, precise information about the scope of the outage can be immediately surfaced and the appropriate response implemented.

Wide Range of Applications

Real-time digital twins can enhance the ability of any stream-processing application to analyze the dynamic behavior of its data sources and respond fast. Here are just a few examples:

  • Intelligent, real-time monitoring: fleet tracking, security monitoring, disaster recovery
  • Financial services: portfolio tracking, wire fraud detection, stock back-testing
  • Internet of Things (IoT): device tracking for manufacturing, vehicles, fixed and mobile devices
  • Healthcare: real-time patient monitoring, medical device tracking and alerting
  • Logistics: real-time inventory reconciliation, manufacturing flow optimization

Real-time digital twins enable real-time streaming analytics that previously could only be performed in offline, batch processing. Here are a few examples:

  • They help IoT applications do a better job of predictive analytics when processing event messages by tracking the parameters of each device, when maintenance was last performed, known anomalies, and much more.
  • They assist healthcare applications in interpreting real-time telemetry, such as blood-pressure and heart-rate readings, in the context of each patient’s medical history, medications, and recent incidents, so that more effective alerts can be generated when care is needed.
  • They enable e-commerce applications to interpret website click-streams with the knowledge of each shopper’s demographics, brand preferences, and recent purchases to make more targeted product recommendations.

An Example in Fleet Tracking

Consider the use of real-time digital twins to track the movement of vehicles in a nationwide car or truck fleet. Each twin can track a specific vehicle using specific contextual information, such as the intended route, the driver’s profile, and the vehicle’s maintenance history; ML algorithms can continuously examine engine and cargo telemetry with predictive analytics. These twins can then alert dispatchers or drivers when problems are detected, such as a lost or erratic driver or impending maintenance issue with a vehicle. In additional, real-time aggregate analysis can detect regional issues affecting several vehicles, such as weather delays and closed highways. By boosting situational awareness, real-time digital twins enable dispatchers to quickly hone in on problems and react within seconds.

digital twin streaming service

Everything in Real Time

The ScaleOut Digital Twin Streaming Service simultaneously analyzes and responds to incoming event messages from data sources while performing aggregate analytics across all data sources. This means that real-time digital twins are tracking devices, they are also reporting aggregate patterns and trends to maximize situational awareness.

Large Workload? Not a Problem

By employing a transparently scalable, fully distributed software architecture in the cloud, the ScaleOut Digital Twin Streaming Service can handle fast-growing workloads while maintaining fast response to data sources. Integrated high availability keeps the service running and protects mission-critical data at all times.

Deeper Introspection for Better Responses

Traditional CEP and stream processing pipelines, such as Apache Storm and Flink, are “stateless,” lacking knowledge about the dynamic state of each data source to help interpret incoming telemetry. Real-time digital twins overcome this limitation by tracking state information for each data source, opening the door to much deeper introspection and more effective responses in real time. These twins can incorporate algorithmic code, rules engines, or even machine learning to help perform their analysis of incoming events.

Easily Build Applications

The Next Generation in Streaming Analytics

Real-time digital twins both simplify the design of stream-processing applications and improve the quality of streaming analytics. The traditional approach relies on partitioning application code into multiple pipeline steps and using ad hoc techniques to access caches or databases.  This adds complexity and puts the burden on the developer to ensure fast performance.

digital twin streaming service

Real-time digital twins sidestep this complexity by offering a simple, straightforward model for processing incoming telemetry based on tracking each data source’s dynamic state. This avoids the need to build streaming pipelines, and the execution platform automatically ensures high throughput and fast response times. The use of well understood, object-oriented development techniques further simplify the design process.

digital twin streaming service

What is a “Real-Time Digital Twin”?

Unlike traditional digital twin models, real-time digital twins focus on analyzing incoming event messages to provide immediate feedback to their data sources (e.g., devices) within a live system. Each twin comprises a state object holding dynamic information about the data source and an application-defined, message-processing method and/or machine learning algorithm that analyzes incoming events and generates outgoing messages and alerts, as depicted in the following diagram:

digital twin streaming service

As event messages flow into the ScaleOut Digital Twin Streaming Service, a digital twin is created for each unique data source to process incoming messages from that data source. The message-processing method uses information in the state object to help analyze each event message and decide what action to take. It can send a message back to the data source and/or send an alert if further action is required. (Some incoming messages may take the form of commands, which can be forwarded to the data source.) The message-processing method also can update the state object to track dynamic changes in the data source and help analyze future events.

The cloud service can simultaneously process incoming messages from many thousands (or even millions) of unique data sources, and it forwards each message to its corresponding real-time digital twin.  In addition, it can perform aggregate analytics across all digital twins by extracting information from the state objects, combining this information, and presenting the results in various types of charts and graphs.

Building Applications with Real-Time Digital Twin Models

The ScaleOut Digital Twin Builder™ software toolkit enables developers to define object-oriented state information and analytics code for tracking telemetry from each type of data source (for example, a wind turbine or a fire alarm). This toolkit provides APIs in Java, C#, and JavaScript for constructing real-time digital twin “models,” which are then deployed to the ScaleOut Digital Twin Streaming Service with just a few clicks in its web-based UI. The ScaleOut Model Development Toolkit™ gives analysts the ability to develop real-time digital twins with easy-to-use business rules instead of code, and it allows machine learning algorithms to be deployed with no code required.

Each model defines the properties to be stored in the state objects and the user-defined analytics code and/or machine learning algorithms needed to process incoming telemetry. Once deployed, the cloud service uses these models to automatically create unique “instances” of real-time digital twins for all data sources as it processes incoming event messages.

Familiar, object-oriented class definitions in C#, Java, and JavaScript simplify the development of advanced analysis algorithms and leverage everything developers already know about object-oriented programming. Equally important, they ensure a clean separation between application-specific code and the platform’s orchestration of event processing. The net result is that applications are straightforward to write and run without the need for specialized knowledge of complex APIs or platform semantics.

The following diagram depicts the real-time digital twin instances created to handle incoming telemetry from cars in a large rental car fleet. Each instance could hold detailed knowledge about each car’s rental contract, the driver’s demographics and driving record, and maintenance issues. With this information, the application’s message-processing method could, for example, alert managers when a driver repeatedly exceeds the speed limit according to criteria specific to the driver’s age and driving history or violates other terms of the rental contract, thus providing new insights on telemetry received from vehicles that otherwise would not be available in real time.

digital twin streaming service

An application can define multiple real-time digital twin models to process telemetry from different types of devices. For example, an application which is analyzing telemetry from the components of a wind turbine might define three real-time digital twins corresponding to different components of the wind turbine, such as blades, generator, and control panel. Each component could send telemetry to three different digital twin instances, one of each type, as illustrated below:

digital twin streaming service

Fast Deployment to the Cloud

The ScaleOut Digital Twin Builder™ software toolkit simplifies the development of Java, C#, and JavaScript-based real-time digital twin models by providing object-oriented classes that serve as a basis for defining these models. The next step is to deploy the models to ScaleOut’s cloud service using a web-based UI. Once deployed, these models await incoming event messages and create real-time digital twin instances as new data sources are detected, as illustrated below:

digital twin streaming service

The ScaleOut Digital Twin Streaming Service’s UI lets the user easily connect the cloud service to numerous popular messaging hubs, including Microsoft Azure IoT Hub, Amazon AWS IoT Core, Kafka, and a REST web service, with more connectors to be released soon. When data sources send event messages to a connected hub, these messages are forwarded to the cloud service. Once authenticated, the cloud service receives incoming event messages and delivers them to their corresponding real-time digital twins. It also sends outgoing messages from twins back to their corresponding data sources using the connected hub. Cloud connections to messaging hubs employ transparent scalability to maximize stream-processing throughput.

digital twin streaming service

Easily Handle Complex Scenarios

Beyond just using real-time digital twins to model physical data sources, they can be organized in a hierarchy to implement subsystems operating at successively higher levels of abstraction within a real-time application. Alerts from lower-level real-time digital twins can be delivered as telemetry to higher-level twins which handle abstracted behaviors.


Seamlessly Migrate to the Edge

IoT applications often need to partition application logic between the cloud and edge to avoid WAN delays. Because of their powerful encapsulation of application logic, real-time digital twins can transparently migrate low-level event-handling functionality to the edge — where the devices live — instead of re-implementing application code.


Maximize Situational Awareness

Aggregate Analytics for Situational Awareness

Real-time applications that track thousands of data sources, such as vehicles in a fleet or sensors on a security perimeter, need to be able to immediately identify hot spots and determine whether a pattern exists. This enables fast, strategic responses to emerging threats and optimal use of scarce resources.

Consider a cyber-security alerting network for a regional power grid. This network may comprise many thousands of software agents distributed across servers and controllers in the network, each assessing logins and commands to determine whether a potential attack is occurring. To respond effectively, it’s critical to immediately determine how widespread an attack is and where resources should be applied to contain it.

Real-time digital twins provide a powerful software architecture for meeting this challenge. The software agents can send telemetry to real-time digital digital twins which track the reporting status of all servers and controllers, including a current assessment of the likelihood of an attack. Real-time aggregate analytics can be performed across all the state objects for all twins to determine the overall  status of the network and, in the case of an attack, which locations have the highest threat assessment. This enables managers to see the complete situation and formulate a fast, strategic response.

No Programming Required

The cloud service’s UI enables fast, easy creation of real-time, aggregate analytics that combine the state of all real-time digital twins of a given type and provide immediate, graphical feedback. Each analytics “widget” displays as a bar, pie, or line chart and updates every few seconds with continuous, real-time results.

digital twin streaming service

Each aggregate analytics operation is easily specified through the UI by selecting the parameters for a continuous “MapReduce” calculation (similar to an Excel pivot table), including:

  • the real-time digital twin model
  • the property within the state objects to be aggregated
  • the aggregation operator (average, min, max, count)
  • an optional property used to group the results
  • the chart type (bar, column, pie, line)

The cloud service runs this operation every five seconds to update the contents of the chart with the latest values from the real-time digital twins. This ensures that users always have the latest information on aggregate state of their digital twins.

In addition, the cloud service’s UI offers powerful query capabilities on the dynamic state of real-time digital twins. Queries run in parallel across all cloud servers to instantly identify which data sources have properties of interest. For example, in the above cyber-security application for a power grid, a query could be used to determine which specific nodes in the network have the highest current risk of attack based on recent network behavior. Queries can be configured to run continuously and plot their results on a geospatial map that maximizes situational awareness.

Because users can aggregate and query state information curated by real-time digital twins instead of just view raw telemetry, they gain much deeper insights on the state of the system they are monitoring. Real-time digital twins ingest and analyze telemetry to dynamically create state information that both filters out transient behaviors and surfaces important patterns and trends that need attention. This ability of real-time digital twins to perform real-time analytics within milliseconds gives users a significantly more accurate picture of the situation and enables them to react more quickly and effectively.

Powerful Execution Platform

The ScaleOut Digital Twin Streaming Service deploys real-time digital twins on a highly scalable, in-memory computing platform in the cloud and runs both stream-processing and aggregate analytics using an integrated compute engine. This delivers fast response times for devices (typically 1-3 milliseconds), and refreshes global statistics every 5 seconds.

ScaleOut’s in-memory computing platform is based on ScaleOut StreamServer and represents more than a decade of development and refinement. Its industry-leading software architecture offers several key benefits for hosting real-time digital twins:

  • automatic correlation of incoming event messages by data source for delivery to their corresponding real-time digital twin instances
  • fast event delivery and response using an internal protocol based on the Reactive Extensions APIs and designed to minimize data motion
  • fast access to state objects using scalable, highly available, memory based storage
  • highly available event processing in conjunction with the connected message hub
  • integrated MapReduce execution designed to minimize data motion
  • scalable event-processing throughput by transparently adding cloud-based servers to keep event processing and aggregate analytics fast as the workload grows



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