Apache Beam Founder Tyler Akidau Discusses Streaming System and Their Complexities

Apache Beam Founder Tyler Akidau Discusses Streaming System and Their Complexities

In this podcast, we are talking to Tyler Akidau, a senior engineer at Google, who leads the technical infrastructure and data processing teams in Seattle, and a founding member of the Apache Beam PMC and a passionate voice in the streaming space. This podcast will cover data streaming and the 2015 DataFlow Model streaming paper [http://www.vldb.org/pvldb/vol8/p1792-Akidau.pdf] and much of the concepts covered, such as why dealing with out-of-order data is important, event time versus processing time, windowing approaches, and finally preview the track he is hosting at QConf SF next week. Why listen to this podcast: - Batch processing and streaming aren’t two incompatible things; they are a function of different windowing options. - Event time and processing time are two different concepts, and may be out of step with each other. - Completeness is knowing that you have processed all the events for a particular window. - Windowing choice can be answered from the what, when, where, how questions. - Unbounded versus bounded data is a better dimension than stream or batch processing. More on this: Quick scan our curated show notes on InfoQ http://bit.ly/2AyBTAb You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq Subscribe: www.youtube.com/infoq Like InfoQ on Facebook: bit.ly/2jmlyG8 Follow on Twitter: twitter.com/InfoQ Follow on LinkedIn: www.linkedin.com/company/infoq Want to see extented shownotes? Check the landing page on InfoQ: http://bit.ly/2AyBTAb

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