Leverage Streaming Data in a Microservices Ecosystem
Imagine a world where operational data is continuously flowing from applications and devices at an extremely high rate. Now imagine services intercepting this data and analyzing it real time. Sounds futuristic? It's not—it's here today. Mark Richards describes what streaming architecture is all about—what it is, when to use it, and how to implement it in a microservices ecosystem. Mark describes the overall ecosystem for streaming architecture—including a brief discussion about the differences in Apache Spark, Flink, and Hadoop—and then explains how Apache Kafka works. Using live coding examples in Kafka, Mark demonstrates some techniques for leveraging streaming data such as metrics gathering for monitoring, distributed logging, request distribution analysis, and threshold analysis in a microservices ecosystem. Join Mark as he discusses some of the implications and limitations of Kafka and the important topic of when to choose Kafka over standard messaging such as JMS, AMQP, and MSMQ.