Digital Twin with Apache Kafka - Simulating of car manufacturing by robots on Siemens
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Apache Kafka as Digital Twin for Open, Scalable, Reliable Industrial IoT (IIoT)

This blog post discusses the benefits of a Digital Twin in Industrial IoT (IIoT) and its relation to…
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Apache Kafka in the Automotive Industry
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Apache Kafka in the Automotive Industry

In November 2019, I had the pleasure to visit “Motor City” Detroit. I met with several automotive companies,…
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IoT Live Demo – 100.000 Connected Cars with Kubernetes, Kafka, MQTT, TensorFlow

Live Demo – 100.000 Connected Cars – Real Time Processing and Analytics with Kubernetes, Kafka, MQTT and TensorFlow leveraging Confluent and HiveMQ.
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Apache Kafka and PLC4X Architecture for IIoT Automation Industry
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Apache Kafka, KSQL and Apache PLC4X for IIoT Data Integration and Processing

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.
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IoT and Supply Chain Optimization with Apache Kafka and Machine Learning
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Apache Kafka and Machine Learning for Real Time Supply Chain Optimization in IIoT

Apache Kafka and Machine Learning for Real Time Supply Chain Optimization: Integrate in real time with the legacy world and proprietary IIoT protocols (like Siemens S7, Modbus, Beckhoff ADS, OPC-UA, et al). You can process the data at scale and then ingest it into a modern database or analytic / machine  learning framework.
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Apache Kafka and Machine Learning
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Apache Kafka + KSQL + TensorFlow for Data Scientists via Python + Jupyter Notebook

Streaming Processing with Apache Kafka and KSQL for Data Scientists via Python and Jupyter Notebooks to build analytic models with TensorFlow and Keras.
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Deep Learning KSQL UDF for Streaming Anomaly Detection of MQTT IoT Sensor Data

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.
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Model Serving: Stream Processing vs. RPC / REST with Java, gRPC, Apache Kafka, TensorFlow

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.
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