This blog post takes a look at cutting edge technologies like Apache Kafka, Kubernetes, Envoy, Linkerd and Istio to implement a cloud-native service mesh for a scalable, robust and observable microservice architecture.
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.
I want to share my slide deck and video recordings from the talk “10 Lessons Learned from Building Cloud Native Middleware Microservices” at O’Reilly Software Architecture April 2017 in New York, USA in April 2017.
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