BaSyx uses PLC4X as a data provider component for bridging PLC protocols into the Asset Administration Shell world.
StreamPipes uses PLC4X as its connectivity layer for S7, Modbus, and other PLC protocols. PLC4X handles the low-level protocol communication while StreamPipes provides the visual pipeline editor and analytics on top.
Malcolm's OpenSearch data can be visualized in Grafana as an alternative to OpenSearch Dashboards, integrating OT security alerts into existing plant monitoring dashboards.
Malcolm embeds Suricata as its signature-based detection engine, running it in IDS mode against live traffic or uploaded PCAPs and indexing all alerts into OpenSearch.
Suricata EVE JSON alert data can be visualized in Grafana via Elasticsearch or Loki, providing real-time security dashboards alongside OT metrics.
Suricata feeds network-level alerts into Wazuh for correlation with host-based events, creating a unified IT/OT security monitoring stack.
StreamPipes can sink processed data to InfluxDB for time-series storage, and read from InfluxDB as a data source for historical analysis pipelines.
StreamPipes processes and enriches IIoT data streams, then routes results to databases like PostgreSQL or InfluxDB where Grafana visualizes them. StreamPipes handles the real-time analytics pipeline; Grafana provides the dashboarding layer.
StreamPipes connects to Mosquitto as an MQTT data source via its built-in MQTT adapter, enabling real-time ingestion of sensor and telemetry data from the factory floor into StreamPipes analytics pipelines.
Grafana subscribes to MQTT topics for real-time dashboards
Node-RED connects to MQTT brokers natively
Node-RED writes sensor data to InfluxDB
Node-RED triggers Grafana annotations
Grafana visualizes ERPNext manufacturing metrics
UMH ships with pre-configured Grafana dashboards for OEE and production monitoring
Grafana reads from InfluxDB via Flux queries
UMH includes Node-RED for custom data flow orchestration and protocol bridging
Node-RED automates ERPNext workflows via REST API
UMH uses Mosquitto as its internal MQTT broker for the Unified Namespace
Foxglove and Grafana can be combined for comprehensive robotics observability, with Foxglove handling multimodal robot data and Grafana monitoring infrastructure metrics.
FreeCAD's CAM workbench generates G-code that can be executed by LinuxCNC. This integration enables a complete open-source workflow from CAD design to CNC machining.
Ignition and Node-RED can exchange data via MQTT, REST APIs, and databases for flexible industrial automation workflows.
Ignition's Historian module can store time-series data in InfluxDB for high-performance analytics and integration with the MING stack.
Ignition connects to Mosquitto MQTT broker via the Cirrus Link MQTT modules for IIoT data collection and edge-to-cloud communication.
Ignition's SQL Bridge and Historian modules enable seamless data export to Grafana for advanced visualization and dashboarding.
Rapid SCADA can send data to Grafana for advanced visualization and dashboarding.
Rapid SCADA can archive data to InfluxDB for time-series storage and analysis.
Rapid SCADA integrates with Mosquitto as an MQTT broker for IIoT device communication and data acquisition.
Siemens Teamcenter PLM integrates with Siemens Opcenter MES to create a seamless digital thread from engineering to manufacturing. This integration enables bi-directional data flow between product design and shop floor execution.
Keras models can be exported to ONNX format for deployment across different inference engines and hardware accelerators. This enables Keras-trained models to run on ONNX Runtime, TensorRT, OpenVINO, and other ONNX-compatible execution providers.
Doppler syncs secrets bidirectionally with AWS Secrets Manager, enabling teams to use Doppler's developer-friendly interface while maintaining compatibility with AWS-native infrastructure.
Keras 3 integrates with PyTorch as one of its three supported backends. When using the PyTorch backend, Keras models use PyTorch tensors and autograd, enabling access to the PyTorch ecosystem including torch.compile, PyTorch Lightning, and native PyTorch deployment options.
Keras 3 integrates with TensorFlow as one of its three supported backends. When using the TensorFlow backend, Keras models compile to TensorFlow operations and can leverage the full TensorFlow ecosystem including TensorBoard, TensorFlow Serving, and TensorFlow Lite for deployment.
HarvesterHarvester can be imported into Rancher Virtualization Management so operators can manage Harvester virtual machines and Kubernetes clusters from the Rancher interface with shared authentication and RBAC.
Rancher can provision and manage K3s clusters as one of its supported Kubernetes distributions. This lets teams operate lightweight edge or resource-constrained Kubernetes deployments through the same Rancher control plane used for larger downstream clusters.