Software Toolbox Technical Blog

In the ever-growing digital landscape of manufacturing operations, single use solutions & applications may solve issues, but also create their own data silos. These silos present multiple challenges (limited visibility, slower decision-making, security blind spots) and lead to missed opportunities due to the complex management of varying solutions. For example, OPC HDA is a standard utilized for collecting and sharing historical data across industrial automation technology solutions. Based on the standards of OPC DA, integration challenges arise due to the dependency of COM and remote DCOM connections, creating incompatibility with newer solutions and technologies.

Today we'll explore how Cogent DataHub was used to solve many of these integration headaches and silos by providing a middleware platform for connectivity between a variety of protocols and technologies. With the latest major revision of Cogent DataHub V11, the ability to collect historical OPC HDA data is now possible with the addition of the OPC HDA Client capability.

Welcome back to the Summer of IoT Blog Series. In this blog we’ll explore how Cogent DataHub’s External Historian and built-in Store and Forward features ensure efficient data flow without risking network security at the plant or enterprise level.

As organizations work toward Industry 4.0 and digital transformation, ensuring secure and reliable OT/IT convergence is essential for breaking down data silos and creating cohesion between your business systems. IoT devices provide real-time data and connectivity to transform traditional industrial operations. Complementing this, historians serve as centralized data repositories that store and organize time-series data from IoT devices and other sources. Together, the data from both fuels advanced analytics, automation, and smart decision-making across the enterprise.

If you’ve ever had to dig into historical data, you know that being able to easily access and understand it is key for gaining insights and decision making. Without the right tools, retrieving and reporting on historical data can quickly turn into a nightmare – leaving you dreaming of a better option. In today’s age of digital transformation, Dream Report gives you easy access to accurate data, allowing you to maintain a competitive edge, make informed decisions, and leave your nightmares in the past. 

In this blog post and accompanying video, you’ll learn how to use TOP Server’s Local Historian Plug-In with Dream Report to quickly capture and visualize historical data—revealing hidden trends in just 8 minutes.

Cogent DataHub® (from our developer partner/publisher Skkynet) provides a wide selection of connectivity options to OPC DA, OPC UA, OPC A&E, Databases, Excel, ODBC, DDE, Linux, Modbus.  As you may have already learned recently, DataHub Version 10 now includes support for integration with a wide range of common external historians. This expands your options for which data sources are compatible for historization in your chosen historian solution beyond the historian's native interfaces.

In this blog post, we'll detail how to get started integrating your data sources accessible by DataHub with AVEVA™ Insight, by writing data to your AVEVA Insight instance while also making that same historical data available for trending, visualization and reporting for both DataHub and external historical clients supporting OPC UA Historical Access (HA). 

Cogent DataHub is known for its extensive connectivity to OPC DA, OPC UA, OPC A&E, Databases, Excel, ODBC, DDE, Linux, Modbus.  As you may have already learned, the DataHub Version 10 now includes support for integration with a wide range of common external historians, expanding your options for which data sources are compatible for historization in your chosen historian solution.

In this blog post, we'll detail how to get started integrating your data sources accessible by DataHub with AVEVA™ Historian by writing data to your AVEVA Historian while also making that same historical data available for trending, visualization and reporting for both DataHub and external historical clients supporting OPC UA Historical Access (HA). 

Cogent DataHub is known for its extensive connectivity to OPC DA, OPC UA, OPC A&E, Databases, Excel, ODBC, DDE, Linux, Modbus.  The latest Version 10 release expands connectivity to include a wide range of common external historian solutions for historizing industrial data and integrating historized data with other systems.

In this blog post, we'll highlight some specific key details about which historians are supported and how other DataHub functionality works with those historians to provide flexible options.

What is the most important feature for any data logger to have? At the end of the day, we are trusting a data logger to take our data and store it.  Now whether that is for reporting, optimization, legal reasons, or simply to retain process data over some period of time in case it might be needed later, we expect the data to be logged without any losses.

This is particularly true in the industrial automation space, where the amount of data that is being logged is growing almost as exponentially as the speed at which the data is needed in order to have a business impact. As such, the prospect of losing data can very quickly result in lost jobs, thousands of dollars in lost production, and legal or financial penalties in case of an audit.

So how much do you trust your data logger to really be logging 100% of the data that it should be? In this blog post, I will address these concerns and highlight the ways OPC UA provides capabilities that software applications can leverage to provide peace of mind.

Does your data logging software force you to define the location where you're logging your data ahead of time?  Wouldn’t it be nice if your logging software could evaluate your data and make a decision on where the data should be logged?

In this first of two posts in a series on dynamic SQL logging, I'll show you how the OPC Data Logger can easily be configured to switch between SQL Tables at runtime, reducing any post-log sorting you have to do in SQL and saving you time and effort in the process.