HighByte Blog
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Read company updates and our technology viewpoints here.
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Time to read: 5 minutes  Posted by Torey Penrod-Cambra 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: 3 minutes  Posted by Tony Paine We live in a time where attacks on critical infrastructure and the underlying software and hardware that comprise these systems is all too common and will only increase year over year. Rest assured that HighByte is committed to putting security first in its design and implementation of its software solutions.
Our industry recognizes that a defense-in-depth strategy must be employed when building out a technology stack from various components. This not only applies to an end-user’s use of applications and equipment from various vendors, but even more so by vendors who develop solutions that pull in third-party technology or tap into interfaces and standards that allow for seamless integration with foreign sources of data and information. Time to read: 7 minutes  Posted by Aron Semle 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: 10 minutes  Posted by John Harrington The promise of Industry 4.0 has many manufacturing leaders thinking big. They envision a future in which real-time access to data opens the door to unprecedented levels of operational flexibility, predictability, and business improvement. For many, early-stage wins often lead to larger projects that stall or fail to scale because their data infrastructure couldn’t support the increasing project complexity.
Enter Industrial DataOps. DataOps (data operations) is the orchestration of people, processes, and technology to securely deliver trusted, ready-to-use data to all the systems and people who require it. The first known mention of the term “DataOps” came from technology consultant and InformationWeek contributing editor Lenny Liebmann in a 2014 blog post titled, “DataOps: Why Big Data Infrastructure Matters.” According to Leibmann: “You can’t simply throw data science over the wall and expect operations to deliver the performance you need in the production environment—any more than you can do the same with application code. That’s why DataOps—the discipline that ensures alignment between data science and infrastructure—is as important to Big Data success as DevOps is to application success.” |
