HighByte Intelligence Hub |
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Contextualized data is essential for Industry 4.0. Deploy HighByte Intelligence Hub at the Edge to access, model, transform, and prepare plant floor data for analysis in the Cloud.
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The Industrial DataOps Solution for Industry 4.0
HighByte Intelligence Hub is an Industrial DataOps software solution designed specifically for industrial data modeling, delivery, and governance.
Built for Industrial Data
HighByte Intelligence Hub was built for the unique qualities of industrial data. The software securely connects devices, files, databases, and systems via open standards and native connections. Use the interface to model streaming data in real time, normalize and standardize data points and data models inherent to diverse machinery, and add context to data payloads that otherwise lack descriptions. Tap into real-time and asset model data from a variety of edge data sources, including machine data, transactional data, and time series (historical) data.
Designed for Scale
HighByte Intelligence Hub was designed for scale. Simplify and accelerate the modeling of tens of thousands of datapoints from PLCs and machine controllers with re-usable models that transform raw data into complex, useful information. Import and export template definitions to quickly replicate common datasets across assets. Efficiently deliver contextualized and correlated information to the applications that require it.
Ideal for Operations
HighByte Intelligence Hub is an ideal solution for manufacturers and other industrial companies because the software was designed for Operational Technology (OT) teams. The platform-agnostic solution runs on-premises at the Edge, scales from embedded to server-grade computing platforms, and offers a code-free user interface. Administrators can network distributed Intelligence Hubs through a single management portal.
Measurable Benefits for OT, IT, and Line of Business
Accelerate analytics and other Industry 4.0 use cases with a digital infrastructure solution built for scale.
✓ Reduce system integration time from months to hours
✓ Improve data curation and preparation for AI and ML applications
✓ Scale operations metrics and analytics across the enterprise
✓ Reduce information wait time for business functions
✓ Eliminate time spent troubleshooting broken integrations
✓ Empower operators with insights from the Cloud
✓ Improve system-wide security and data governance
✓ Meet system integrity and regulatory traceability requirements
✓ Reduce Cloud ingest, processing, storage costs, and complexity
✓ Optimize data payloads for specific target applications and use cases
✓ Improve data curation and preparation for AI and ML applications
✓ Scale operations metrics and analytics across the enterprise
✓ Reduce information wait time for business functions
✓ Eliminate time spent troubleshooting broken integrations
✓ Empower operators with insights from the Cloud
✓ Improve system-wide security and data governance
✓ Meet system integrity and regulatory traceability requirements
✓ Reduce Cloud ingest, processing, storage costs, and complexity
✓ Optimize data payloads for specific target applications and use cases
Connect, Condition, Model, and Flow
1. ConnectionsConnect to inputs and publish outputs with a few clicks—no scripting involved.
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2. ConditionsCreate aggregates, deadbands, and custom conditions to transform raw data.
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3. ModelsDevelop models that standardize and contextualize industrial data.
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4. FlowsControl and monitor the flow of industrial data to consuming applications.
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The Features You Need for an Agile Infrastructure
Codeless Integration
Collect and publish data over open standards and native connections—eliminating the need for custom-coded integrations. Easily configure and manage multiple connections and their respective inputs and outputs within the script-free interface. Collect data from SQL and REST source systems using dynamic requests leveraging inputs from other systems. Quickly integrate data from specialty systems and devices. Merge data from multiple systems into a complex modeled payload.
Data Conditioning
Collect raw input data, condition the data, and pass conditioned data to instances or flows. Filter data through a deadband condition to reduce the jitter in a source sensor or measurement. Filter the data through an aggregate to buffer higher resolution data and provide statistical calculations using average, min, max, count, and delta at a slower rate to characterize the specified time period. Manipulate and transform raw input data into usable information. Alarm on bad quality or stale data.
Data Transformations
Use the built-in transformation engine based on JavaScript notation to standardize and normalize data for comparison and application mismatches. The transformation engine enables you to perform calculations, execute logic to define new “virtual property” values, and decompose complex strings at the Edge to improve data usability and reduce transmission volume. Define global JavaScript functions or load third-party JavaScript or Node packages, then use them in any expression within the Intelligence Hub.
Data Modeling
Represent machines, products, processes, and systems with key intelligent data models suited to your needs. Contextualize thousands of industrial data points by merging them with information from other systems, adding meta data, standardizing data attribute names and lists, and normalizing units of measure. Model hundreds of common assets in minutes with templatized inputs and instances.
Connection Flows
Create data flows for raw data, modeled information or files between connections at any frequency or event. Enable store and forward to buffer data on disk if target connection is lost. Manage data flows within HighByte Intelligence Hub, monitoring flow state and key metrics. Trigger flows for any asset based on third-party templates. See and be alerted to connection and flow failures through the Intelligence Hub and easily monitor the Intelligence Hub at scale using third-party system-monitoring applications.
Data Pipelines
Use the graphical data Pipelines builder to configure the steps to curate complex data payloads for systems like Amazon S3 and Azure Blob Storage. The builder includes steps to buffer, transform, and format payloads and the ability to track the transformation of data through the pipeline.
MQTT Broker
Use the embedded MQTT broker that’s tightly integrated with the Intelligence Hub’s core data integration and contextualization capabilities to design a namespace that provides a real-time view of the state of business. The MQTT v3.1.1 and v5 compliant broker was built from the ground up by HighByte. It supports both JSON and Sparkplug payloads and can be quickly enabled in the UI. The broker is a critical component to rapidly building a local unified namespace (UNS) inside the factory.
REST Data Server
The REST Data Server acts as an API gateway for industrial data residing in OT systems, so any application or service with an HTTP client can securely request OT data in raw or modeled form directly from the Intelligence Hub—without requiring domain knowledge of the underlying systems. This API exposes the Intelligence Hub’s connections, models, and instances as well as the underlying values. Connect to the REST Data Server to access the Intelligence Hub as a transactional, request-and- response interface and programmatically browse the full Industrial DataOps infrastructure.
Edge Deployment
Run HighByte Intelligence Hub on your choice of light-weight hardware platforms including single board computers, industrial switches, IoT gateways, and industrial data servers. Deploy as an individual software installation or Docker image, which allows you to rapidly deploy and upgrade system software components.
