Conference: March 5-7, 2018

Workshops: March 8-9, 2018

Tyler Akidau
Engineer @Google & Founder/Committer on Apache Beam

Tyler Akidau is a senior staff software engineer at Google, where he is the technical lead for the Data Processing Languages & Systems group, responsible for Google's Apache Beam efforts, Google Cloud Dataflow, and internal data processing tools like Google Flume, MapReduce, and MillWheel. His also a founding member of the Apache Beam PMC. Though deeply passionate and vocal about the capabilities and importance of stream processing, he is a firm believer in batch and streaming as two sides of the same coin, with the real endgame for data processing systems the seamless merging between the two. He is the author of the 2015 Dataflow Model paper, the Streaming 101 and Streaming 102 articles, and the upcoming Streaming Systems book. His preferred mode of transportation is by cargo bike, with his two young daughters in tow.

Find Tyler Akidau at

@takidau

Talk : Streaming SQL Foundations: Why I ❤ Streams+Tables

Track : Stream Processing in the Modern Age
Location : Whittle, 3rd flr.
Day of week : Monday
Duration : 4:10pm - 5:00pm

What does it mean to execute robust streaming queries in SQL? What is the relationship of streaming queries to classic relational queries? Are streams and tables the same thing conceptually, or different? And how does all of this relate to the programmatic frameworks like we’re all familiar with? This talk will address all of those questions in two parts.

First, we’ll explore the relationship between the Beam Model (as described in The Dataflow Model paper and the Streaming 101 and Streaming 102 blog posts) and stream & table theory (as popularized by Martin Kleppmann and Jay Kreps, amongst others, but essentially originating out of the database world). It turns out that stream & table theory does an illuminating job of describing the low-level concepts that underlie the Beam Model.

Second, we’ll apply our clear understanding of that relationship towards explaining what is required to provide robust stream processing support in SQL. We’ll discuss concrete efforts that have been made in this area by the Apache Beam, Calcite, and Flink communities, compare to other offerings such as Apache Kafka’s KSQL and Apache Spark’s Structured streaming, and talk about new ideas yet to come. In the end, you can expect to have a much better understanding of the key concepts underpinning data processing, regardless of whether that data processing batch or streaming, SQL or programmatic, as well as a concrete notion of what robust stream processing in SQL looks like.

Other talks from track Stream Processing in the Modern Age

Streaming Reactive Systems & Data Pipes w. squbs
Principal MTS, Architect @PayPal
Akara Sucharitakul
Streaming Reactive Systems & Data Pipes w. squbs
Software Engineer @PayPal
Anil Gursel
Lessons From a ~Yearly Re-Write of a Data Pipeline
Lead Architect @Qubit
Jibran Saithi
Drivetribe: A Social Network on Streams
CTO @Drivetribe
Aris Koliopoulos
Drivetribe: A Social Network on Streams
Backend engineer @Drivetribe
Hamish Dickson
Next Steps in Stateful Streaming with Apache Flink
Committer @ApacheFlink, CTO @dataArtisans
Stephan Ewen

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