Tyler Akidau is a senior staff software engineer at Google Seattle, where he leads technical infrastructure internal data processing teams for MillWheel and Flume. Tyler is a founding member of the Apache Beam PMC and has spent the last seven years working on massive-scale data processing systems. Though deeply passionate and vocal about the capabilities and importance of stream processing, he is also a firm believer that batch and streaming are two sides of the same coin and that the real endgame for data processing systems is the seamless merging between the two. He is the author of the 2015 “Dataflow Model” paper and “Streaming 101” and “Streaming 102” blog posts. His preferred mode of transportation is by cargo bike, with his two young daughters in tow.
Frances Perry is a software engineer who likes to make big data processing easy, intuitive, and efficient. After many years working on Google’s internal data processing stack, Frances joined the Cloud Dataflow team to make this technology available to external cloud customers. She led the early work on Dataflow’s unified batch/streaming programming model and is on the PMC for Apache Beam.
Kenn Knowles is a founding committer of Apache Beam (incubating). Kenn has been working on Google Cloud Dataflow—Google’s Beam backend—since 2014. Prior to that, he built backends for startups such as Cityspan, Inkling, and Dimagi. Kenn holds a PhD in programming languages from the University of California, Santa Cruz.
Slava Chernyak is a senior software engineer at Google. Slava spent over five years working on Google’s internal massive-scale streaming data processing systems and has since become involved with designing and building Google Cloud Dataflow Streaming from the ground up. Slava is passionate about making massive-scale stream processing available and useful to a broader audience. When he is not working on streaming systems, Slava is out enjoying the natural beauty of the Pacific Northwest.
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