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Apache Kafka: Online Talk Series

A Practical Guide to Selecting a Stream Processing Technology - 5 out of 6

Thursday, December 1, 2016

10:00am PT | 1:00pm ET | 7:00pm CET

Recording Time: 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 SeriesRecorded on December 1, 2016.

Autor

Michael Noll

Product Manager

Michael is a principal technologist in the Office of the CTO at Confluent, the company founded by the creators of Apache Kafka. He focuses on longer-term product and technology strategy. Previously, Michael was the lead product manager for stream processing at Confluent, where his team created Kafka Streams and the streaming database ksqlDB. He is a well-known technology blogger in the big data community (www.michael-noll.com) and a committer/contributor to open source projects such as Apache Storm and Apache Kafka.

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