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40:32

Netflix Keystone - How We Built a 700B/day Stream Processing Cloud Platform in a Year

Posted by Peter Bakas  on  May 19, 2016

Peter Bakas presents in detail how Netflix has used Kafka, Samza, Docker, and Linux to implement a multi-tenant pipeline processing 700B events/day in the Amazon AWS cloud.

41:26

Hunting Criminals with Hybrid Analytics

Posted by David Talby  on  May 10, 2016

David Talby demos using Python libraries to build a ML model for fraud detection, scaling it up to billions of events using Spark, and what it took to make the system perform and ready for production.

47:42

Resilient Predictive Data Pipelines

Posted by Sid Anand  on  May 06, 2016

Sid Anand discusses how Agari is applying big data best practices to the problem of securing its customers from email-born threats, presenting a system that leverages big data in the cloud.

35:44

Big-Data Analytics Misconceptions

Posted by Irad Ben-Gal  on  May 03, 2016

Irad Ben-Gal discusses Big Data analytics misconceptions, presenting a technology predicting consumer behavior patterns that can be translated into wins, revenue gains, and localized assortments.

39:15

How Comcast Uses Data Science and ML to Improve the Customer Experience

Posted by Jan Neumann  on  May 01, 2016 1

Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.

50:50

Immutable Infrastructure: Rise of the Machine Images

Posted by Axel Fontaine  on  Apr 24, 2016 2

Axel Fontaine looks at what Immutable Infrastructure is and how it affects scaling, logging, sessions, configuration, service discovery and more.

36:19

The Mechanics of Testing Large Data Pipelines

Posted by Mathieu Bastian  on  Apr 24, 2016

Mathieu Bastian explores the mechanics of unit, integration, data and performance testing for large, complex data workflows, along with the tools for Hadoop, Pig and Spark.

56:38

How to Have Your Causality and Wall Clocks Too

Posted by Jon Moore  on  Apr 10, 2016

Jon Moore talks about distributed monotonic clocks (DMC) whose timestamps can reflect causality but which have a component that stays close to wall clock time.

38:27

Stream Processing with Apache Flink

Posted by Robert Metzger  on  Apr 07, 2016

Robert Metzger provides an overview of the Apache Flink internals and its streaming-first philosophy, as well as the programming APIs.

43:44

Rethinking Streaming Analytics for Scale

Posted by Helena Edelson  on  Apr 03, 2016

Helena Edelson addresses new architectures emerging for large scale streaming analytics based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK) or Apache Flink or GearPump.

43:44

Monkeys in Lab Coats: Applying Failure Testing Research @Netflix

Posted by Peter Alvaro, Kolton Andrus  on  Mar 24, 2016

The authors present how lineage-driven fault injection evolved from a theoretical model into an automated failure testing system that leverages Netflix’s fault injection and tracing infrastructures.

51:04

#NetflixEverywhere Global Architecture

Posted by Josh Evans  on  Mar 23, 2016

Josh Evans discusses architectural patterns used by Netflix to enable seamless, multi-region traffic management, reliable, fast data propagation, and efficient service infrastructure.

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