Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA
Fabian Hueske

Fabian Hueske
Software Engineer, data Artisans

@fhueske

Fabian Hueske is a committer and PMC member of the Apache Flink project. He was one of the three original authors of the Stratosphere research system, from which Apache Flink was forked in 2014. Fabian is a cofounder of data Artisans, a Berlin-based startup devoted to fostering Flink, where he works as a software engineer and contributes to Apache Flink. He holds a PhD in computer science from TU Berlin and is currently spending a lot of his time writing a book, Stream Processing with Apache Flink.

Sessions

5:10pm–5:50pm Wednesday, March 7, 2018
Streaming SQL to unify batch and stream processing: Theory and practice with Apache Flink at Uber
Data engineering and architecture, Streaming systems and real-time applications
 Location: 230 A
Fabian Hueske (data Artisans), Shuyi Chen (Uber)
Average rating: *****
(5.00, 1 rating)
Fabian Hueske and Shuyi Chen explore SQL's role in the world of streaming data and its implementation in Apache Flink and cover fundamental concepts, such as streaming semantics, event time, and incremental results. They also share their experience using Flink SQL in production at Uber, explaining how Uber leverages Flink SQL to solve its unique business challenges. Read more.
2:40pm–3:20pm Thursday, March 8, 2018
Unified and elastic batch and stream processing with Pravega and Apache Flink
Data engineering and architecture, Streaming systems and real-time applications
 Location: 230 A
Secondary topics:  Graphs and Time-series
Fabian Hueske (data Artisans), Flavio Junqueira (Dell EMC)
Average rating: ***..
(3.33, 3 ratings)
Flavio Junqueira and Fabian Hueske detail an open source streaming data stack consisting of Pravega (stream storage) and Apache Flink (computation on streams) that offers an unprecedented way of handling “everything as a stream” that includes unbounded streaming storage and unified batch and streaming abstraction and dynamically accommodates workload variations in a novel way. Read more.