Recent Discussions
Real-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.
Ingesting Data into Veeva Vault CRM via SnapLogic – Alternatives to SFDC Snaps
We are currently in the process of migrating from our existing Veeva CRM (Salesforce-based) platform to Veeva Vault CRM. In our current integration landscape, we use SnapLogic to ingest data from our Specialty Pharma SFTP source into Veeva CRM, leveraging the Salesforce (SFDC) snaps for data ingestion and transformation. However, as we transition to Vault CRM, we’ve identified a gap—SnapLogic does not currently provide a native Snap pack for Veeva Vault CRM. We understand that support for Vault CRM is on SnapLogic’s product roadmap, but it is not expected in the immediate future. As part of our integration planning, we are reaching out to the SnapLogic community and experts to explore the following: Are there any existing Snap packs (e.g., REST, HTTP Client, SOAP, or JDBC snaps) that can be configured to support integration with Vault CRM? Has anyone implemented custom pipelines or reusable components for Vault CRM ingestion using generic SnapLogic snaps? Any known limitations, authentication considerations or Vault-specific constraints we should be aware of when building these integrations? We greatly appreciate any insights, lessons learned, or recommendations from those who have explored similar integration use cases. Thank you in advance for your time and input.
SFTP: Worked ok then settings changed not not working
We have set up a new SFTP site and we had access to it via Snap – read, write, delete. All was good. We had to change some settings for our client to access it and now SnapLogic cannot connect. I’m getting “Unable to create filesystem object for sftp://xxxxxxx/xxxx/xxx”. I’m using a directory browser to test the connections. I’m able to connect with the same user and password via command line. I made sure the connection string is accurate. Any thoughts? Thanks, Bob
SQS Consumer snap is ready all message at pipeline startup
Hi, I have certain observations while running a pipeline with SQS snap as trigger. 1. On startup, pipeline reads all available messages from sqs and hold them in buffer and process them as per concurrency 2. There is no mechanism to control the speed at which we poll messages from queue. If you have used sqs consumer snap in your pipelines, how did you tackle above issues. Any throttling mechanism did you implement to control this. Thanks, Vishal.
Using Windows Authentication with SQL Server Accounts
The topic of "providing windows ID / Password to SQL Server has come up a few different times so below is a solution that I found to work in most instances. Scenario: Service accounts with Native SQL Server authentication is NOT supported (internal security rules, regulatory compliance etc.) Running SnapLogic using “IntegratedSecurity = true” would require every database to grant access to an individual robot / service account and didn’t allow specific user tracking Solution: 3rd party JDBC driver called jtds (http://jtds.sourceforge.net/) allows you to specify URL Properties and pass in account credentials. You will need to upload this jar file into the SLDB and reference that within the account settings (see screen caps below). On the SQL Server, you can see Windows Authentication selection for ID “SQLSvr_TESTING” Account settings within SnapLogic for “SQL Server Account” type: Account settings that need to be defined Referencing the JAR files uploaded to the SLDB The JDBC Driver Class should be set to net.sourceforge.jtds.jdbc.Driver URL Properties Defined: useNTLMv2 = true domain = [YourDomain] instance = [InstanceName-OPTIONAL] NOTE : It’s important to stipulate that if the users windows password changes, they will need to come into SnapLogic and adjust the password in the account as well. By default, the SQL Server snaps will attempt to reconnect 3 times so you may get into a situation where the ID becomes locked out.
SFTP errors
FYI I was getting errors with pipelines using a directory browser with a basic auth account for SFTP. After doing some testing and verifying the authentication details I ended up rolling back the Binary Snap Pack version to Main19844. Problem was resolved - must be a bug in new version. The error I was getting is Failure: Unable to create filesystem object for sftp://sftp.accorplus.com:2222/, Reason: Failed to get SFTP session connected, Resolution: Please check all properties and credentialsDownload Multiple files/ or view in the browser
how can i download multiple files using a rest get where by in the url i passed split values of different files, the response shows there are four files however when executing the pipeline as triggered task , only the first file gets downloaded this is the final out put of the mapperDifference among SAP, SAP S/4hana and SAP S/4hana cloud snap packs
Hello, We are working on collating some information on SAP snap packs and related ones. Can anyone from snaplogic community help explaining the differences among SAP, SAP S/4hana and SAP S/4hana cloud snap packs ? Any kind of help is appreciated. Thank you!
