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R, In-Memory Databases, Big Data, & Pizza - next Wednesday, April 3rd

From: Michael E. D.
Sent on: Tuesday, March 26, 2013 10:58 PM

Dear Bay Area R Users,

For those of you interested in a Meetup showcasing how to use R with open-source, in-memory databases (think: an open source alternative to the Tableau + Vertica paradigm), here's a cross-posted Meetup next Wednesday, April 3rd.

(Confession:  Like BARUG sponsors Revolution and RStudio, I am not without some bias, since Druid was developed and open-sourced by my company, Metamarkets.  That said, we have no commercial interest in selling database software, hence our decision to distribute it freely).


R, In-Memory Databases, Big Data, & Pizza

Wednesday, April 3rd, 5:30pm at 625 2nd St, SF, CA

One of the longstanding limitations of R is that its native data structures require keeping data local and in-memory.  An increasingly number of packages have extended R's reach into more scalable backends, but the challenge (and rationale for R's in-memory bias) still remains:  interacting with disk-backed data stores is slow, not interactive.

A new generation of in-memory data stores holds promise to alleviate this bottleneck.  At Metamarkets, we've recently open-sourced one variant, called Druid, as well as an R-connector for it. Netflix and dozens of others have successfully deployed Druid in production environments, ingesting and querying up to 10,000 events per second (the largest running Druid cluster houses 20TB of queryable data).

At this Meetup, we'll showcase some of the use cases for combining R's powerful statistical and visualization capabilities on a local client with Druid's sub-second queries over billions of data points in a cloud-hosted, remote data store.

Outside of production deployments, Metamarkets leverages Druid for internal analytics: enabling query and drill down on extremely large data sets in order to provide insight on everything from cluster health metrics to customer use patterns.

Join us at our San Francisco office, bring your laptops, and learn how to query large-scale data with R and an open-source data store.

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