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

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