IoT devices generate large amounts of data that must be continuously processed and analyzed. Apache Kafka is a highly scalable open source streaming platform for reading, storing, processing and forwarding large amounts of data from thousands of IoT devices. KSQL is an open source streaming SQL engine based natively on Apache Kafka to enable stream processing for everyone using simple SQL commands.
This talk shows with a scenario from the health care sector how Kafka and KSQL can help to continuously conduct health checks of patients. A live demo shows how machine learning models – trained with frameworks such as TensorFlow, DeepLearning4J or H2O – can be deployed into a runtime-critical and scalable real-time application.
Knowledge of distributed systems and architectures is helpful. Experience with machine learning is helpful, but not essential.
* Apache Kafka is a streaming platform for reading, storing, processing and forwarding large volumes of data from thousands of IoT devices.
* KSQL allows continuous integration and analysis without external big data clusters and without writing source code.
* Machine learning models can be easily trained and used in the Apache Kafka environment.
is a Developer Advocate at Confluent as well as an Oracle ACE Director and Developer Champion. His career has always involved data, from the old worlds of COBOL and DB2, through the worlds of Oracle and Hadoop, and into the current world with Kafka. His particular interests are analytics, systems architecture, performance testing and optimization. He blogs at https://www.confluent.io/blog/author/robin/ and http://rmoff.net/. Outside of work he enjoys drinking good beer and eating fried breakfasts, although generally not at the same time.