HighByte Blog
Read company updates and our technology viewpoints here.
|
Read company updates and our technology viewpoints here.
|
Elevate your industrial interoperability: A primer on data Pipelines in the Intelligence Hub3/8/2024
Time to read: 15 minutes In HighByte Intelligence Hub, the Pipelines feature was created to make modeled data consumable by a diverse range of applications and services. With the last few releases of the Intelligence Hub, Pipelines has undergone big changes to further that goal and more. From adding new functionality to refining the UX, Pipelines has swiftly evolved beyond its initial focus on “post-processing” payloads for advanced use cases. It has become a core data engineering capability to solve industrial interoperability problems within the Intelligence Hub. 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: 9 minutes In an earlier blog, “The power of payloads in your unified namespace,” I discussed the use of complex payloads combining multiple unified namespace (UNS) data streams to make the architecture more responsive to the diverse needs of consuming personas and systems. In this post, I want to show what these complex payloads might look like, how data models can enable a UNS architecture, and how easily HighByte Intelligence Hub can provide consuming systems with the necessary data—when and how it’s needed. Time to read: 9 minutes I consistently hear that many manufacturers are drowning in data and struggling to make it useful. Why is that? A modern industrial facility can easily produce more than a terabyte of data each day. With a wave of new technologies for artificial intelligence and machine learning coupled with real-time dashboards and prescriptive insights, industrial companies should be seeing huge gains in productivity. Unplanned asset and production line maintenance should be a thing of the past. But we know that is not the case. Access to data does not make it useful. Industrial data is raw and must be made fit for purpose to extract its true value. Furthermore, the tools used to make the data fit for purpose must operate at the scale of an industrial enterprise. For many industrial companies, this is a daunting task requiring alignment of people, process, and technology across a global footprint and supply chain. At HighByte, we’re putting our best foot forward to solve this data architecture and contextualization problem from a technology perspective. But what about people and process? To pull it all together, we recently published a new guide, “Think Big, Start Small, Scale Fast: The Data Engineering Workbook.” The guide provides 10 steps to achieving a scalable data architecture based on the best practices we’ve learned from our customers over the last several years. Time to read: 8 minutes For quite some time, people have been asking us how the Intelligence Hub relates to a Unified Namespace (UNS). Is it a specific part of a UNS architecture, a platform by which one might build a UNS, or a UNS architecture itself? Over time, our answers have developed alongside the capabilities of the Intelligence Hub. From the beginning, the Intelligence Hub could connect to third-party MQTT brokers as well as model the data going in and out of them. And recently, we added an embedded MQTT broker to the Intelligence Hub to address brokering and provide the ability for MQTT clients to connect to the Intelligence Hub directly. These provided the functionality needed for much of what might facilitate or be considered a UNS, but it was missing a critical part: a way to visualize the contents. |
|