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.
I am happy that my first official Confluent blog post was published and want to link to it from by blog:
How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka
Case Study: How to Move from a (Middleware) Monolith to Cloud, Containers and Microservices leveraging Docker, Cloud Foundry, Kubernetes, Consul, Hystrix, API Management, and others cool things.
Comparison of Open Source IoT Integration Frameworks such as Eclipse Kura (+ Apache Camel), Node-RED, Flogo, Apache Nifi, StreamSets, and others… (slide and video recording)
Introduction to the Open Source IoT Integration Project Flogo to build very lightweight edge applications and microservices for cloud native containers / cloud platforms / serverless architectures.