Bridging Legacy OPC Classic Servers(DA, AE, HDA) to SnapLogic via OPC UA Wrapper
Despite significant advances in industrial automation, many critical devices still rely on legacy OPC Classic servers (DA, AE, HDA). Integrating these aging systems with modern platforms presents challenges such as protocol incompatibility and the absence of native OPC UA support. Meanwhile, modern integration and analytics platforms increasingly depend on OPC UA for secure, scalable connectivity. This post addresses these challenges by demonstrating how the OPC UA Wrapper can seamlessly bridge OPC Classic servers to SnapLogic. Through a practical use case—detecting missing reset anomalies in saw-toothed wave signals from an OPC Simulation DA Server—you’ll discover how to enable real-time monitoring and alerting without costly infrastructure upgradesIndustrial IoT – Turbine Lubrication Oil Level Monitoring & Alert Mechanism via OPC UA and SnapLogic
In the energy sector, turbine lubrication oil is mission-critical. A drop in oil level or pressure can silently escalate into major failures, unplanned shutdowns, and expensive maintenance windows. In this blog, we showcase a real-world implementation using SnapLogic and OPC UA, designed to: 🔧 Continuously monitor turbine lubrication oil levels 📥 Ingest real-time sensor data from industrial systems 📊 Store telemetry in data lakes for analytics and compliance 📣 Real-time Slack alerts to engineers — before failures strike This IIoT-driven solution empowers energy providers to adopt predictive maintenance practices and reduce operational riskIndustrial IoT – OPC UA Real-Time Motor Overheat Detection and Auto-Shutdown Using SnapLogic
Industrial motors are critical assets in manufacturing and process industries, where overheating can result in costly downtime or catastrophic failure. In this blog, we demonstrate how SnapLogic and OPC UA were used to build a real-time, event-driven pipeline that detects motor overheating, initiates an automated shutdown, logs events for auditing, and notifies the maintenance/engineering teamReal-Time Flow Control Event Analytics and Predictive Maintenance using SnapLogic and OPC UA
Overview In industrial plants, flow control valves play a critical role in maintaining safe and efficient operations by regulating the flow of steam, gas, or liquids through turbines and auxiliary systems. However, even minor valve performance issues — such as delayed actuation, partial closure, or sensor faults — can trigger cascading operational problems across the system. Without a real-time event detection and analytics mechanism, these issues often remain unnoticed until they cause visible production impact or downtime. Engineers traditionally rely on manual monitoring or post-failure analysis, which leads to: Delayed Detection of Flow Anomalies Lack of Root-Cause Visibility Unplanned Downtime and Maintenance Costs No Predictive Maintenance Capability To overcome these challenges, we implemented a real-time data integration pipeline using SnapLogic and OPC UA, enabling event-driven monitoring, automated data capture, and intelligent analytics in Snowflake Use Case Summary When a flow control valve in the turbine system is triggered, it generates an event in the OPC UA server. The SnapLogic pipeline, built with the OPC UA Subscribe Snap, detects this event instantly. Once the event is received, the Snaplogic pipeline reads live data from Sensor OPC UA nodes, including: Pressure Temperature Flow Rate Controller Status All these values are combined with the OPC UA server timestamp into a single unified record. The record is then stored in Snowflake for historical tracking, trend analysis, and real-time analytics dashboards. Workflow: Snaplogic Pipeline Workflow: Parent Pipeline: Subscribe to Flow Control Data events Child Pipeline: Capture sensor node details and aggregate Data Subscribe to Flow Control Data events using OPCUA Subscribe: parent SnapLogic pipeline, “Headless Ultra”, is designed to run continuously (indefinitely) as a background monitoring service. Its primary role is to capture all real-time flow control data events from the OPC UA server using the OPC UA Subscribe Snap. Parameter Value Description Pipeline Type Headless Ultra The pipeline is deployed in Ultra Task mode without any frontend or manual trigger. It runs as a persistent listener to capture OPC UA events in real-time. Execution Duration Indefinite The pipeline never stops unless explicitly terminated. This ensures continuous data monitoring and streaming. Snap Used OPC UA Subscribe Snap This Snap subscribes to specific OPC UA nodes (like flow control valve, pressure, temperature, and controller status) and receives event updates from the OPC UA server. Publish Interval 1000 milliseconds (1 second) Defines how often the OPC UA server sends updates to the subscriber. Every 1 second, the Snap receives the latest data from the subscribed nodes. Monitoring Mode Reporting In “Reporting” mode, the Subscribe Snap reports value changes or events whenever an update occurs, ensuring that only meaningful data changes are captured — not redundant values. Queue Size 2 The number of unprocessed event messages that can be queued at once. A queue size of 2 ensures lightweight buffering while maintaining near real-time responsiveness. If new events arrive faster than processing speed, older ones are replaced, preventing data backlog. Capture Real-Time node values from Sensor Nodes and load data to Snowflake Warehouse Child pipeline collects diagnostic context by fetching live data from related OPC UA sensor nodes and consolidates them into a single analytical record before loading it into Snowflake for historical analysis and predictive maintenance Select Sensor Nodes using OPC UA Node Selector snap Read live data from Sensor Nodes using OPCUA Read Snap Group all Sensor node values to single record using Group By N snap Combine Flow Control value details and Sensor node values to single record Output: Node Type What Happens After Trigger Why It’s Important Pressure Node (Sensor.Pressure) The current pressure is read and stored with the event. Helps determine if over pressure caused the flow control valve to trigger. Temperature Node (Sensor.Temperature) Captured as part of the same record. High temperature may indicate overheating, cavitation, or pump issues. Flow Rate Node (Sensor.FlowRate) Logged when the trigger occurs. Confirms whether the actual flow exceeded or dropped below the threshold. Controller/Motor Status (Controller.Status) Captured to show control logic state (ON, OFF, FAULT). Correlates actuator or PLC state with the trigger condition. Server Timestamp Captured from the OPC UA event source. Ensures temporal accuracy for event reconstruction and trend analysis. Write data into Snowflake warehouse 📊 Analytics Dashboard Overview The analytics dashboard is powered by data ingested and processed through SnapLogic OPC UA pipelines and stored in Snowflake. It provides real-time visibility, trend analytics, and predictive insights to help operations and reliability teams monitor and optimize industrial equipment performance Event Stream: Displays real-time flow control valve events captured by the SnapLogic OPC UA Subscribe Snap in the parent Headless Ultra pipeline Sensor Trends: Visualizes time-series data from multiple OPC UA sensor nodes related to flow control — including pressure, temperature and flow rate Predictive Insights: Highlights machine learning–driven predictions and risk scores derived from historical flow control event data like Predicted Downtime, Anomaly Scores etc System Health Summary: Displays the overall health and operational status of the monitored flow system Conclusion This use case demonstrates how SnapLogic’s intelligent integration capabilities, combined with OPC UA data streams and Snowflake’s analytical power, can transform raw industrial sensor data into actionable insights. By automating the ingestion, transformation, and visualization of real-time flow control events, temperature, and pressure data, the solution enables engineers to detect anomalies early, predict potential equipment issues, and make informed operational decisions. The analytics dashboard provides a consolidated view through Event Streams, Sensor Trends, Predictive Insights, and System Health Summaries, helping organizations move from reactive monitoring to proactive and predictive maintenance. In essence, this architecture proves how data integration and AI-driven analytics can empower industrial enterprises to enhance reliability, optimize performance, and reduce downtime — paving the way toward truly smart, data-driven operations.
