This post explores use cases and architectures for processing data in motion with Apache Kafka in Industrial IoT (IIoT) across verticals such as automotive, energy, steel manufacturing, oil&gas, cybersecurity, shipping, logistics. Use cases include predictive maintenance, quality assurance, track and track, real-time locating system (RTLS), asset tracking, customer 360, and more. Examples include BMW, Bosch, Baader, Intel, Porsche, and Devon.
Apache Kafka and MQTT are a perfect combination for many IoT use cases. This blog series covers the pros and cons of both technologies. Various use cases across industries, including connected vehicles, manufacturing, mobility services, and smart city are explored. The examples use different architectures, including lightweight edge scenarios, hybrid integrations, and serverless cloud solutions. This post is part two: Connected Vehicles and V2X applications.
The digital transformation connects the world. People, vehicles, factories, cities, digital services, and other “things” communicate with each other in real-time to provide a safe environment, efficient processes, and a fantastic user experience. This scenario only works well with data processing in real-time at scale. This blog post shares a presentation that explains why Apache Kafka plays a key role not just in one of these industries or use cases, but also to connect the different stakeholders to each other.
Apache Kafka is NOT hard real-time in Industrial IoT or vehicles (such as autonomous cars) but integrates the OT/IT world for near real-time data correlation and analytics in hybrid architectures across factories at the edge, multiple clouds, and over countries.
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