Data integration and processing in Industrial IoT (IIoT, aka Industry 4.0 or Automation Industry). Apache Kafka, its ecosystem (Kafka Connect, KSQL) and Apache PLC4X are a great open source choice to implement this integration end to end in a scalable, reliable and flexible way.
Machine Learning / Deep Learning models can be used in different ways to do predictions. Natively in the application or hosted in a remote model server. Then you combine stream processing with RPC / Request-Response paradigm. This blog post shows examples of stream processing vs. RPC model serving using Java, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving.
JavaOne 2016 Trends: Besides focus on Java platform updates (Java 9, Java EE 8, etc.), I saw three hot topics, which are highly related to each other: Microservices, Docker and Cloud. I also talked about this topic from a middleware perspective. See my slides and lessons learned.
This article shows the different components available for a Hybrid Integration Architecture. The goal is not to discuss different vendor offerings but to explain different concepts and benefits of each component in general and how they relate to each other. Including concepts such as Hybrid Integration Platform (HIP), Cloud-Native Middleware, PaaS, Docker, iPaaS, iSaaS, API Management, and others.
The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this.