This meetup focuses on Scalability and technologies to enable handling large amounts of data: Hadoop, HBase, distributed NoSQL databases, and more!
There's not only a focus on technology, but also everything surrounding it including operations, management, business use cases, and more.
We've had great success in the past, and are growing quickly! Previous guests were from Twitter, LinkedIn, Amazon, Cloudant, Microsoft, 10gen/MongoDB, and more.
This month's guests:
Nick Kypreos, PhD, CERN / LHC
How LHC experiments process hundreds of TB/s coming from multiple subsystems, getting it into the hands of thousands of users across the globe with the help of machine learning, and keeping it all going with various datacenters across the globe.
William L. Bain, PhD. Founder and CEO of ScaleOut Software, Inc.
Hadoop has been widely embraced for its ability to economically store and analyze large data sets. Using parallel computing techniques like MapReduce, Hadoop can reduce long computation times to hours or minutes. This works well for mining large volumes of historical data stored on disk, but it is not suitable for gaining real-time insights from live operational data. Still, the idea of using Hadoop for real-time data analytics on live data is appealing because it leverages existing programming skills and infrastructure – and the parallel architecture of Hadoop itself.
This presentation will describe how real-time analytics using Hadoop can be performed by combining an in-memory data grid (IMDG) with an integrated, stand-alone Hadoop MapReduce execution engine. This new technology delivers fast results for live data and also accelerates the analysis of large, static data sets.
Our format is flexible: We usually have 2 speakers who talk for ~30 minutes each and then do Q+A plus discussion (about 45 minutes each talk) finish by 8:45.
There'll be beer afterwards, of course!
WhitePages,[masked]th Avenue #1600, Seattle, WA
Rock Bottom Brewery
Doors open 30 minutes ahead of show-time. Please show up at least 15 minutes early out of respect for our first speaker.