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While it may be convenient to follow simple steps to get connectivity working for your IIoT solution, sometimes you are better off having an understanding of the elements that make up the broad spectrum of connectivity technologies.
Developed at LinkedIn in 2010, Apache Kafka - a stream processing engine, now powers web-scale Internet companies such as Netflix, Uber, Twitter, Airbnb, and more. Profoundly impacting real-time user experience. Of equal impact, is its application in creating a continuous streaming pipeline of manufacturing data, from the factory-floor to data centers. Fundamentally changing the structural organisation of manufacturing systems. To gain an understanding of Kafka in IIoT, I had a conversation with Kai Waehner. Kai is Field CTO and Global Technology Advisor at Confluent, a company that was founded by Kafka creators and is behind the open-source project. Here are the contents of our discussion ✔️ Stream processing in Manufacturing ✔️ Apache Kafka and Its Role in IIoT ✔️ Kafka vs MQTT ✔️ Architecture Patterns for Kafka Deployments ✔️ Connectivity to Industrial Control Systems ✔️ Data Ingestion to enterprise Applications ✔️ Examples of Real-Time Streaming Analytics ✔️ Using Kafka as a Data Historian ✔️ Re-Engineering ERP Suites with Kafka ✔️ Using Kafka to Drive Machine Learning ✔️ Hybrid Kafka Deployments ✔️ Kafka as a Platform for the Digital Twin ✔️ Kafka's Role in Augmented Reality ✔️ Kafka Use Cases in Manufacturing ✔️ Confluent
So here's the thing, data from industrial sources is inherently messy. For example, a typical PLC system manages thousands of tags from both physical instruments and internal calculations, but this data is often unstructured, not linked to a unifying data model, and uses naming conventions that are vague to the outside world.
Cliché as it may sound, data IS the new Oil. But, to fully reap the benefits, data needs to be properly collected and advanced analytics correctly applied to it.
While the connectivity of industrial systems is the most important aspect of IIoT, there is currently a confusing mix of connectivity technologies and standards.
The success of IIoT in mission-critical applications depends on its ability to support local storage, compute, and connectivity for real-time responses, while sending selected data to the cloud for additional analytics. In short, Edge Computing
Having a solid understanding of the key components of an IIoT architecture and how to integrate them is the most important aspect of building an IIoT application, both as an in-house solution and as a service offering
Digital Twins will, undoubtedly, transform how manufacturers build and maintain products. From consumer goods to complex structures such as buildings and aircraft.
Here's what we discussed
From the publisher's feed
AI in Manufacturing is an Industry40.tv podcast hosted by Kudzai Manditereza, bringing together industry leaders, technologists and…
The podcast covers the architectures and real-world implementations shaping industrial AI, including connectivity, industrial data infrastructure, semantic technologies, data platforms, AI agents and operational applications.
For manufacturing engineers, architects and technology leaders working to turn industrial data and AI into measurable operational impact.