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.
KSQL UDF for sensor analytics. Leverages the new API features of KSQL to build UDF / UDAF functions easily with Java to do continuous stream processing with Apache Kafka. Use Case: Connected Cars – Real Time Streaming Analytics using Deep Learning.
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.
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