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.
Apache Kafka is the central nervous system of many applications in various areas related to the automotive and manufacturing industry for processing analytical and transactional data in motion across edge, hybrid, and multi-cloud deployments. This article explores the event streaming landscape for automotive including connected vehicles, smart manufacturing, supply chain optimization, aftersales, mobility services, and innovative new business models.
Apache Kafka is the de facto standard for event streaming to process data in motion. This blog post explores when NOT to use Apache Kafka. What use cases are not a good fit for Kafka? What limitations does Kafka have? How to qualify Kafka out as it is not the right tool for the job?
This blog post explores how event streaming with Apache Kafka enables IoT analytics for cost savings, better consumer experience, and reduced risk in real estate and smart buildings. Examples include improved real estate maintenance and operations, smarter energy consumptions, optimized space usage, better employee experience, and better defense against cyber attacks.
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.
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 three: Manufacturing, Industrial IoT, and Industry 4.0.
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.
A smart city is an urban area that uses different types of electronic Internet of Things (IoT) sensors to collect data and then use insights gained from that data to manage assets, resources, and services efficiently. This blog post explores how Apache Kafka fits into the smart city architecture and the benefits and use cases.
The energy industry is changing from system-centric to smaller-scale and distributed smart grids and microgrids. These smart grids require a flexible, scalable, elastic, and reliable cloud-native infrastructure for real-time data integration and processing. This post explores use cases, architectures, and real-world deployments of event streaming with Apache Kafka in the energy industry to implement smart grids and real-time end-to-end integration.