Uniting Machine Learning and Data Streaming Technologies Part 2

This post explores the integration of machine learning (ML) with data streaming technologies, focusing on bridging the JVM and Python ecosystems. It discusses the role of SQL (ksqlDB, Flink SQL) as a declarative language for building data pipelines and the limitations of SQL's composability. The post details strategies for inter-language communication, such as PyFlink and PySpark using Py4J, and Flink's process and thread modes. It also examines current integration patterns with ML experiment management platforms and provides a Python client code example for building a real-time ML-powered microservice using Confluent Python Client.