A smart factory organizes itself without human intervention to produce the desired products. This blog post explores how data streaming powered by Apache Kafka helps connect and move data to the cloud at scale in real-time, including a case study from BMW and a simple lightboard video about the related enterprise architecture.
In the IoT world, MQTT and OPC UA have established themselves as open and platform-independent standards for data exchange in Industrial IoT and Industry 4.0 use cases. Data Streaming with Apache Kafka is the data hub for integrating and processing massive volumes of data at any scale in real-time. This blog post explores the relationship between Kafka and the IoT protocols, when to use which technology, and why sometimes HTTP/REST is the better choice. The end explores real-world case studies from Audi and BMW.
The manufacturing industry is moving away from just selling machinery, devices, and other hardware. Software and services increase revenue and margins. Equipment-as-a-Service (EaaS) even outsources the maintenance to the vendor. This paradigm shift is only possible with reliable and scalable real-time data processing leveraging an event streaming platform such as Apache Kafka. This post explores how Kafka-native Condition Monitoring and Predictive Maintenance help with this innovation.
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.