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50:44

Ingest & Stream Processing - What Will You Choose?

Posted by Pat Patterson, Ted Malaska  on  Aug 14, 2016 1

Pat Patterson and Ted Malaska talk about current and emerging data processing technologies, and the various ways of achieving "at least once" and "exactly once" timely data processing.

30:44

Monitoring and Troubleshooting Real-Time Data Pipelines

Posted by Alan Ngai, Premal Shah  on  Jul 20, 2016

Alan Ngai and Premal Shah discuss best practices on monitoring ​distributed real-time data processing frameworks and how DevOps can gain control and visibility over these data pipelines.

39:53

Connecting Stream Processors to Databases

Posted by Gian Merlino  on  Apr 03, 2016

Gian Merlino discusses stream processors and a common use case - keeping databases up to date-, the challenges they present, with examples from Kafka, Storm, Samza, Druid, and others.

48:46

How 30 Years of Ticket Transaction Data Helps you Discover New Shows!

Posted by Vaclav Petricek  on  Aug 19, 2015

Vaclav Petricek discusses how to train models, architect and build a scalable system powered by Storm, Hadoop, Spark, Spring Boot and Vowpal Wabbit that meets SLAs measured in tens of milliseconds.

01:03:55

Scalable Big Data Stream Processing with Storm and Groovy

Posted by Eugene Dvorkin  on  Jan 18, 2015

Eugene Dvorkin provides an introduction to Storm framework, explains how to build real-time applications on top of Storm with Groovy, how to process data from Twitter in real-time, etc.

46:49

Data & Infrastructure at Airbnb

Posted by Brenden Matthews  on  Dec 31, 2013

Brenden Matthews describes the infrastructure built at Airbnb using Mesos in order to support Hadoop and Storm.

44:03

Lessons Learned Building Storm

Posted by Nathan Marz  on  Aug 11, 2013 2

Nathan Marz shares lessons learned building Storm, an open-source, distributed, real-time computation system.

Storm: Distributed and Fault-Tolerant Real-time Computation

Posted by Nathan Marz  on  Jan 04, 2013

Nathan Marz introduces Twitter Storm, outlining its architecture and use cases, and takes a look at future features to be made available.

Clojure + Datomic + Storm = Zolodeck

Posted by Amit Rathore  on  Dec 04, 2012

Amit Rathore describes the architecture of Zolodeck, a virtual relationship manager built on Clojure, Datomic, and Storm.

MapReduce and Its Discontents

Posted by Dean Wampler  on  Oct 05, 2012 1

Dean Wampler discusses the strengths and weaknesses of MapReduce, and the newer variants for big data processing: Pregel and Storm.

Storm: Distributed and Fault-tolerant Real-time Computation

Posted by Nathan Marz  on  Jul 25, 2012

Nathan Marz discusses Storm concepts –streams, spouts, bolts, topologies-, explaining how to use Storms’ Clojure DSL for real-time stream processing, distributed RPS and continuous computations.

Storm: Distributed and Fault-tolerant Real-time Computation

Posted by Nathan Marz  on  Oct 21, 2011 1

Nathan Marz explain Storm, a distributed fault-tolerant and real-time computational system currently used by Twitter to keep statistics on user clicks for every URL and domain.

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