Past Meetup

Flink Forward San Fran Sneak Peak Double Feature!

This Meetup is past

32 people went

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Details

Flink Forward San Francisco is coming up April 11-12. CHAF's very own Joe Olson, Trevor Grant, and Dean Wampler are all speaking. Joe and Trevor are going to give sneak peaks of their talk / give themselves a dead line so for once they aren't scrambling to slap together slides and demos until 3am the night before they present (instead they'll be slapping together slides and demos the night before this meetup).

At anyrate- come out, eat some pizza, learn something, and give us feedback.

It's a really exciting time to be involved with Apache Flink, and the Chicago Community is arguably one of the strongest in the Western Hemisphere.

Each talk should be 30 minutes-ish. Since we are prepping for conferences we'll be trying to adhere strictly to this. All Q&A / Feedback will be at the end.

Using Flink and Queryable State to Buffer High Frequency Time Series Data
Joe Olson

Flink’s streaming API can be used to construct a scalable, fault tolerant framework for buffering high frequency time series data, with the goal being to output larger, immutable blocks of data. As the data is being buffered into larger blocks, Flink’s queryable state feature can be used to service requests for data still in the “buffering” state. The high frequency time series dataset in this example is electro cardiogram data (EKG) that is buffered from a sample rate in milliseconds into multi-minute blocks.

Joe Olson Bio (http://sf.flink-forward.org/kb_speakers/joe-olson/)

[Short Break ~ 5 min]

Introduction to Online Machine Learning Algorithms
Trevor Grant

Online algorithms are an increasingly popular yet often misunderstood branch of machine learning, where model parameter estimates are updated for each new piece of information received. While mini-batch methods have often been mislabeled as ‘streaming-machine learning’, true online methods have different implementations and goals. This talk will explain key differences between online and offline machine learning, an introduction to many common online algorithms, and how online algorithms can be analyzed. An example using Apache Flink to detect trends on Twitter will be presented. Attendees will come away from this talk with a better understanding of the challenges and opportunities from working with online algorithms and how they can begin implementing their own algorithms in Apache Flink.

Trevor Grant Bio (http://sf.flink-forward.org/kb_speakers/trevor-grant-2/)