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September 2019 Bay Area Flink Meetup

Sep 24, 2019 · San Francisco, United States of America

I'm happy to announce that the next Bay Area Apache Flink Meetup is scheduled on Sept. 24, 2019 at Yelp, 140 New Montgomery in San Francisco.


6:30 - Door open
6:30 - 7:00 PM Networking and Refreshments
7:00 - 8:30 PM Short talks

-- Two years of Flink @ Yelp (Enrico Canzonieri and Nishith Mehta, 30m)
Abstract: Yelp has been using Flink for over 2 years to power the entire Data Pipeline infrastructure. We’ve built several components to power a modular architecture that allows our users to solve complex use cases that translate to critical product features. In this talk we’ll first present some of the components and why we’ve built them. We’ll then discuss how our Flink deployment strategy and infrastructure has evolved over time to meet a constant increase in demand and scale.

-- Building a Fraud Monitoring Engine for Instant Payment (David Massart)
Abstract: In Europe, the recent adoption of SEPA Instant Credit Transfers has reduced the time allowed for transferring money to payment beneficiaries from one business day to a maximum of five seconds. This has dramatically reduced time for fraud detection. This presentation will describe how Apache Flink was used to build a fraud monitoring engine that detects and reacts to fraud attempts in real-time. It will discuss the main challenges met during the construction of the engine and the solutions adopted to overcome them. Also discussed will be how the machine learning models used for obtaining fraud detection are regularly retrained, tested, and deployed. These models are completed by a set of detection rules that permit quick reaction to new fraudulent modus operandi.

Event organizers
  • Bay Area Apache Flink Meetup

    A meetup group for all SF Bay Area users and developers of Apache Flink. Apache Flink is an open source platform for scalable batch and stream data processing. Flink combines the scalability and programming flexibility of distributed MapReduce-like platforms with the efficiency, out-of-core execution, and query optimization capabilities found in parallel databases.

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