A Practical Guide to Selecting a Stream Processing Technology - 1:00:55
Why are there so many stream processing frameworks that each define their own terminology? Are the components of each comparable? Why do you need to know about spouts or DStreams just to process a simple sequence of records? Depending on your application’s requirements, you may not need a full framework at all.
Processing and understanding your data to create business value is the ultimate goal of a stream data platform. In this talk we will survey the stream processing landscape, the dimensions along which to evaluate stream processing technologies, and how they integrate with Apache Kafka. Particularly, we will learn how Kafka Streams, the built-in stream processing engine of Apache Kafka, compares to other stream processing systems that require a separate processing infrastructure.
This is talk 5 out of 6 from the Kafka Talk Series. Recorded on December 1, 2016.
Michael Noll, Product Manager, Confluent
Michael Noll is a product manager at Confluent working on Kafka Streams. Previously, Michael was the technical lead of the Big Data platform of .COM/.NET DNS operator Verisign, where he grew the Hadoop, Kafka, and Storm based infrastructure from zero to PetaByte-sized production clusters spanning multiple data centers – one of the largest Big Data infrastructures operated from Europe at the time. He is an experienced tech speaker and avid tech blogger in the Big Data community (www.michael-noll.com) and serves as a technical reviewer for publishers such as Manning in his spare time. Michael received a Master’s in Computer Science and Business, as well as a PhD in Computer Science.