Meetup FastData : Flink SQL in Action, Low latency decision for Bot and Flink goes Cloud Native
Pierre Bittner WeeFin’s CEO shared his experience on how he uses Amazon Web Services on public cloud with Apache Flink for advanced data analysis.
Fabien Hueske from Data Artisan shocased Flink SQL in action
Benjamin Fabre & Philippe Loulidi from DataDome explained how they built a low latency decision loop with ApacheFlink for bot detection.
Presentations in detail:
Flink <3 AWS
Pierre Bittner (@BittnerPierre), WeeFin (@weefin_) Cofounder & CEO/CTO
In this talk, he gave REX of their recent move to AWS services for our Data Pipeline. As our engineering team were spending a lot of time to setup a resilient data stack on Mesos and Kubernetes, how we leverage on AWS managed service (EKS, Kinesis, Elasticsearch,… ), serverless and Flink to focus on business value and stay innovative.
Flink SQL in Action Fabian Hueske (@fhueske) – PMC member of @ApacheFlink – Co-Founder of @DataArtisans
Stream processing is rapidly adopted by the enterprise. While in the past, stream processing frameworks mostly provided Java or Scala-based APIs, stream processing with SQL is recently gaining a lot of attention because it makes stream processing accessible to non-programmers and significantly reduces the effort to solve common tasks.
About three years ago, the Apache Flink community started adding SQL support to process static and streaming data in a unified fashion. Today, Flink SQL powers production systems at Alibaba, Huawei, Lyft, and Uber.
In this talk, he discussed the current state of Flink’s SQL support and explained the importance of Flink’s unified approach to process static and streaming data. Once the basics are covered, he presented common real-world use cases ranging from low-latency ETL to pattern detection and demonstrated how easily they can be addressed by Flink SQL.
Engineering low latency decision loops for bot management
Benjamin Fabre (@bfabre), DataDome (@data_dome) Cofounder & CTO, Philippe Loulidi, lead R&D engineer
As bot threats become more and more complex and massively distributed, they are getting harder to detect. At DataDome, they analyze 2 billion hits per day to protect all the vulnerability endpoints of our customers’ websites and applications. They shared their use case, on how they are doing event analysis using Flink/Kafka to detect new threats with low latency.
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