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Bring devices, sensors, machines, PLCs, applications and enterprise systems together through a unified IoT integration environment.
Bring devices, sensors, machines, PLCs, applications and enterprise systems together through a unified IoT integration environment.
Ingest, filter, aggregate, transform and standardise streaming data before delivering it to the systems and applications that require it.
Use analytics, machine learning, event-driven architecture and visualisation to identify patterns, events, anomalies and operational conditions.
Trigger alerts, automated actions, workflows and application responses based on connected data, events and defined business logic.
Bring devices, sensors, machines, systems, applications and data sources together through one connected integration environment.
The source describes a connector library with hundreds of pre-built integrations and connectivity to more than 800 data sources, helping organisations reduce the complexity of connecting diverse systems.
Capture streaming data and move it through filtering, aggregation, transformation and transmission processes before delivering it to downstream applications and users.
Support MQTT, CoAP, HTTP, OPC, DDE, vendor protocols and open interfaces to accommodate diverse industrial connectivity requirements.
Use APIs and integration capabilities to establish interoperability between traditional manufacturing systems and contemporary IoT technologies.
Monitor connected devices and operational events through flexible dashboards, real-time tracking, visualisations, reports and alerts.
Identify communication errors and irregularities between devices and cloud environments to support faster investigation and response.
Use event-driven architecture, WebSockets, MQTT, webhooks and Change Data Capture to support responsive data exchange and system interactions.
Support secure communication through encryption, authentication and authorisation mechanisms, including TLS/SSL and OAuth-based security approaches.
Combine real-time and historical data with analytics and machine learning to identify patterns, trends and potential future conditions.
Integrate HVAC, Smart Energy and Emission Monitoring environments to connect IoT data with broader sustainability and environmental initiatives.
Use machine learning, deep learning and automation capabilities to streamline data collection, processing and interactions between connected systems.
Establish an IoT integration environment capable of supporting expanding device networks, increasing data volumes, additional applications and evolving Industry 4.0 requirements.