HighByte Intelligence Hub is the Edge-native DataOps solution built for industrial data. The Intelligence Hub enables manufacturers to securely connect, merge, model, and flow industrial data to analytics and enterprise systems on AWS Cloud without writing or maintaining code. The Intelligence Hub seamlessly publishes ready-to-consume industrial data to many AWS services.

Run the application at the Edge to bridge the gap between IT and Operational Technology (OT) teams and systems. 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.

Edge-to-Cloud
Deploy HighByte Intelligence Hub inside the factory and push rich data models to AWS in minutes. The Intelligence Hub connects to data sources using common factory protocols like OPC UA, SQL, MQTT, Sparkplug, REST, and CSV. Easily map these data sources to rich data models using a simple drag-and-drop interface and then send these models to multiple AWS endpoints, including AWS IoT Core, IoT SiteWise, IoT Greengrass, Amazon Kinesis, Amazon S3, and more.

Cloud-to-Edge
HighByte Intelligence Hub can also move data in AWS back to the factory floor, enabling customers to deliver alerts from machine learning, data from third-party sensors, and more. Connect the Intelligence Hub to AWS IoT Core or IoT Greengrass and subscribe to a data topic. This will allow the Intelligence Hub to pull data back to the factory, model it, and send it to HMIs, historians, and other industrial systems using protocols like OPC UA and Sparkplug.

Return on Investment

1. Performance. Drive manufacturing performance, quality, productivity, and waste reduction with curated, modeled data for advanced analytics.
2. Time. Reduce time to deploy and maintain system integrations and accelerate data preparation for analytics and other business systems.
3. Scalability. Build a data infrastructure designed for multi-site scalability and govern data standards across the enterprise.
4. Security. Improve system-wide security and produce audit logs for system integrity and regulatory traceability.