HighByte Blog
Read company updates and our technology viewpoints here.
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Read company updates and our technology viewpoints here.
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Time to read: 11 minutes Since the third industrial revolution, manufacturers have been slowly but steadily automating their operations. From the PLC to ERP to Computer-Integrated Manufacturing (CIM), industry has adopted myriad advancements in technology to gain an edge and, sometimes, even to survive. Rapid technological advancement led to a slew of devices, applications, and services that were rarely introduced or managed under a holistic technology strategy. Time to read: 10 minutes One of the most common concerns I hear regarding the Unified Namespace (UNS) is the architecture lacks the versatility needed to address diverse downstream data consumers. Suppose quality, maintenance, and process engineers are all doing their part to support a production line. Quality teams need inspection results, maintenance teams need asset performance data, and process engineers need lot and process parameters. The teams need data sets that both overlap and differ by use case. These engineers are using different applications and services—anything from ERP modules for quality and maintenance to specialized ML platforms in the cloud—that each require very different data structures. Many of these applications and services do not easily interoperate with the UNS and its architectural conventions. They may not natively interface with MQTT brokers, nor should one expect them to. They may not consume payloads that were oriented around rigid asset hierarchy and publishing telemetry data from process control nodes. They may have completely different needs than what was envisioned when factory automation was installed and integrated. Their data needs can transcend how the UNS was initially architected and organized. HighByte Intelligence Hub can overcome these challenges. Through data modeling and pipelines, the Intelligence Hub enables the full potential of the UNS, delivering contextualized manufacturing data to the cloud. Let’s look at a sample architecture to see how. Time to read: 6 minutes Ever since the first release of HighByte Intelligence Hub in 2020, HighByte has developed the solution to meet the industrial data integration needs of today’s industrial customers and tomorrow’s market requirements. The first version of the Intelligence Hub was a client-based application that collected and published contextualized data to any consuming system. As Industry 4.0 leaders began to integrate more systems and assets into their ecosystems, data consumers needed better data visibility and access. The consumers wanted to be able to see all available information, so they could access exactly what they needed. To deliver this visibility and access, we embedded an MQTT broker into the Intelligence Hub, giving administrators the necessary tools to build a Unified Namespace (UNS) that would allow consumers to easily subscribe to the information they desired. With changing needs in mind, in May 2023, we took the next step in the evolution of the Intelligence Hub, adding the ability to request data on-demand through the Intelligence Hub with a built-in REST Data Server. This addition allows users to request time series, transactional, or master data from systems through a single, simple API. Time to read: 8 minutes "We're not in Kansas anymore." In fact, we’re in Chicago at IMTS, the largest North American Industrial Technology show of the year, where we’re announcing the release of HighByte Intelligence Hub version 2.5! The latest version has come a long way since its original release in early 2020. What began as a data hub for OPC to MQTT has evolved into an Intelligence Hub that truly lives up to its name. The Intelligence Hub satisfies real, complex business use cases and enables enterprise IT management of industrial data. Manufacturers have realized that publishing 750,000 data tags (or even 75,000 data tags) straight to the cloud at a 1 second rate does not solve real business problems for the enterprise and often creates a data swamp. Instead, industrial data must be collected, transformed, aggregated, and delivered to the cloud as curated data payloads. In HighByte Intelligence Hub version 2.5, we’ve provided unparalleled support for Microsoft Azure with the addition of Azure IoT Edge connectivity, support for Azure IoT Central, and the ability to import DTDL models from Azure Digital Twins. We’ve also also added support for third-party JavaScript functions, input cache management, instance referencing, and many more features to improve the coverage of more complex use cases that customers need to solve. As industrial data has become the major source of data for the broader business to drive improvement and new lines of revenue, the systems feeding the cloud for these initiatives must fall within the enterprise IT infrastructure. With the latest release of the Intelligence Hub, we’ve added support for Active Directory and improved ability to monitor and alert when data is bad or stale and when flow performance is poor. This post provides an overview of the release, highlighting new connectivity, monitoring, management, and advanced use case capabilities. Let’s take a look. Time to read: 9 minutes It’s been inspiring to see the wide variety of ways customers are using HighByte Intelligence Hub to conquer Industry 4.0 use cases that previously seemed impossible. From creating contextualized electronic batch reports to improving first run yield, predicting asset maintenance, performing real-time analytics on UNS data, and gaining enterprise-wide performance visibility across multiple sites with different systems—customers are using the Intelligence Hub in increasingly more sophisticated ways. With more sophisticated use cases comes the need for more sophisticated tools for scalability and connectivity in the Intelligence Hub. That’s why I am so excited to introduce HighByte Intelligence Hub version 2.4. With new instance and input templates and parameters, global functions, custom conditions, OPC collection, and more, the latest release takes a giant leap forward in terms of scalability and data pipeline automation. I sat down with my friend and colleague John Harrington, Chief Product Officer at HighByte, to learn more about version 2.4 and what these new capabilities will mean for our customers. Time to read: 5 minutes The real value of Industry 4.0 is realized when manufacturers unlock the power of analytics. With the addition of artificial intelligence (AI) and machine learning (ML), organizations can transform raw data into predictive, meaningful insights. But manufacturers must first overcome a structural barrier to connect their process historians with their analytical applications. This is where HighByte Intelligence Hub comes into play. The latest release of the Intelligence Hub extends connectivity from enterprise systems to historian and time-series database applications, including PI System and InfluxDB. It removes a major disconnect between operational technologies (OT) and the business systems where organizational leaders access the information to make strategic decisions. Time to read: 7 minutes When I first joined the HighByte team, I knew two things. First, modeling industrial data is immensely powerful. After spending a decade interacting with tags and seeing firsthand how building context from tags in the Cloud is painful, I knew that modeling data at the Edge would be a game changer. The second thing I knew is that we were going to build a lot of connectors. This is par for the course in the industrial world where a mix of legacy and new equipment is the norm. We started with the most common and generalized standards, like OPC UA, HTTP, MQTT, and SQL to cast a wide net for connectivity options inside the factory. But it was clear that as we progressed, the market would demand explicit connectors for common systems. That is why I am excited to announce new connectors in version 2.2 for OSIsoft PI System (now part of the AVEVA portfolio), InfluxDB, and Oracle Database. All three connectors support both reading and writing data, and interacting with these systems in advanced ways, without needing to read a manual. Time to read: 7 minutes I love the chaos of an early market like DataOps for Manufacturing. It’s clear that things are changing, but what technologies and approaches will win out is less obvious. In these types of markets, as a solution provider, it’s equally fun to watch them mature. One sign of a maturing market is the type of questions early customers ask about a solution. At first, the questions are different variations of “Does it work?” or “How is it different than a, b, or c?” as customers try and understand the solution and how it solves their problem. As the market matures, the questions shift focus to technical requirements like “What’s the performance with 10,000x?” or “Does it support high availability?” Here at HighByte we’re seeing more scale and reliability questions in early engagements, a sign that both the market and the product are maturing. That’s why I’m excited to announce some key features in version 2.1 that make HighByte Intelligence Hub more scalable and reliable to fit the needs of your production environment. Time to read: 7 minutes Industry 4.0 solutions start with the same problem. How do I collect critical data from the factory floor? This sounds easy, but in reality, factory floors are highly heterogenous environments. It's common to have a newer machine that is highly connected sitting next to a 30-year-old machine with no connectivity at all. This forces teams to get creative. They might use an OPC UA server for one machine, SQL for the next, and retrofit another with new sensors that publish data via REST or MQTT. Each situation is unique, and teams need flexible solutions to leverage the connectivity options they have in place today. That’s why I am excited to announce the release of HighByte Intelligence Hub version 1.3. This release is full of new capabilities that allow our customers to gather data from many sources in the factory, rapidly add context to the data, and reliably deliver it to their platforms of choice. These additional capabilities greatly expand the connectivity options available to our customers. Here are the highlights:
Time to read: 6 minutes
A modern industrial facility can easily produce a terabyte of data each day. With the proliferation of sensors and the recent wave of real-time dash-boarding, artificial intelligence, and machine learning technologies, we should be seeing huge productivity gains. Unplanned maintenance of assets and production lines should be obsolete.
But this is not the case. Access to data does not mean it is useful. Industrial data is very raw and must be made “fit for purpose” in order to extract its true value. Furthermore, the tools used to make the data fit for purpose must operate at the scale of an industrial facility. With these realities in mind, I’ve written a practical, seven-step guide for manufacturers and other industrial companies to make their data fit for purpose. |
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