Flink Forward San Francisco Preview (part 3 of 6): Apache Flink Use Cases
Hey stream processing squirrels! Last week we shared a preview of the keynotes for the upcoming Flink Forward San Francisco. In this post we highlight the sessions about Apache Flink use cases. Learn how companies running stream processing at scale are using Flink.
If you haven’t registered for the conference, you can do it here.
- Ken Krugler from Scale Unlimited will discuss how to use Flink’s support for AsyncFunctions and iterations to build an efficient and scalable web crawler that continuously performs a focused web crawl with no additional infrastructure.
- Garrett Li and Ralph Su will share how their team at eBay built a dynamically configurable alterting engine on top of Flink that ingests and processes metrics, events, and logs from various sources.
- Faraz Babar is going to demonstrate a prototype the team at American Express developed using Apache Kafka and Apache Flink to reconcile and balance transactions across micro-service boundaries.
- Aris Koliopoulos and Alex Garella from DriveTribe will explain how their team uses the events streams technique in production with Flink to successfully design distributed and highly scalable applications and data platforms.
- Xu Yang is going to share the Alibaba team’s experiences developing algorithms on Apache Flink and building web UI to help non-technical data analysts to apply data analysis algorithms and to train machine learning model.
- Dave Torok and Sameer Wadkar will share best practices and lessons learned from embedding Flink in Comcast’s operationalized machine learning lifecycle.
- Xingzhong Xu will introduce how Uber leverages Apache Flink to automatically maintain marketplace reliability, generate market insights, and improve its network efficiency across more than 600 cities in realtime.
- Andrew Gao and Jeff Sharpe are going to review Capital One’s journey to bring fraud decisioning to their tellers using Apache Flink, Kafka, and AWS Lambda. They will share their findings on solving common machine learning problems.
- David Reniz and Dahyr Vergara from Everis will introduce their real-time monitoring and visualization system based on Apache Flink, Apache Hadoop, YARN, Apache Kafka, Nifi, and Druid to track customer experience with web browsing and video stream.