December 4, 2019 | Episode 71

Machine Learning with Kafka Streams, Kafka Connect, and ksqlDB ft. Kai Waehner

  • Notes

In this episode, Kai Waehner (Senior Systems Engineer, Confluent) defines machine learning in depth, describes the architecture of his dream machine learning pipeline, shares about its relevance to Apache Kafka®, Kafka Connect, ksqlDB, and the related ecosystem, and discusses the importance of security and fraud detection. He also covers Kafka use cases, including an example of how Kafka Streams and TensorFlow provide predictive analytics for connected cars.

EPISODE LINKS

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Episode 72December 12, 2019 | 30 min

Ask Confluent #16: ksqlDB Edition

Vinoth Chandar and Gwen Shapira discuss what ksqlDB is, the kinds of applications that you can build with it, vulnerabilities, and various ksqlDB use cases. They also talk about what's currently the best version of Apache Kafka version for performance improvements that don’t cause breaking changes to existing Kafka configuration and functionality.

Episode 73December 16, 2019 | 53 min

Cloud-Native Patterns with Cornelia Davis

Host Tim Berglund catches up with Cornelia Davis, author of Cloud-Native Patterns and VP of Technology at Pivotal, on what cloud-native patterns are, the example code she created, her latest book, and how she wrote the book for the customers she interacts with on a daily basis.

Episode 74December 16, 2019 | 15 min

Apache Kafka 2.4 – Overview of Latest Features, Updates, and KIPs

Apache Kafka 2.4 includes new Kafka Core developments and improvements to Kafka Streams and Kafka Connect, including MirrorMaker 2.0, RocksDB metrics, and more.

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