Storm makes it easy to write and scale complex realtime computations on a cluster of computers, doing for realtime processing what Hadoop did for batch processing. Storm guarantees that every message will be processed. And it’s fast — you can process millions of messages per second with a small cluster. Best of all, you can write Storm topologies using any programming language. Twitter relies upon Storm for much of its analytics.
After being open-sourced, Storm instantly attracted a large community. It is by far the most watched JVM project on GitHub and the mailing list is active with over 300 users.
Storm has a wide range of use cases, from stream processing to continuous computation to distributed RPC. In this talk I’ll introduce Storm and show how easy it is to use for realtime computation.
Nathan Marz is the lead engineer on Twitter’s Publisher Analytics team. He was previously the lead engineer at BackType before being acquired by Twitter in July of 2011.
Nathan is the author of numerous open-source projects relied upon by companies all around the world. These include Cascalog, ElephantDB, and Storm.
He has spoken about his work at conferences such as the Hadoop Summit, Strange Loop, Gluecon, Clojure/conj, and POSSCON. He writes a blog at http://nathanmarz.com.
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